Análisis AFC del CADRI
Brian N. Peña Calero
20/5/2020
Se cargan las librerías a utilizar para el presente análisis
1 Importación de datos y tratamiento de datos
data1 <- haven::read_sav("1. BD/BD_1ER ENVIO.sav")
data2 <- haven::read_sav("1. BD/BD_Colegio Jose Granda.sav")1.1 Guardar las etiquetas
Se guardan las etiquetas asociadas a los datos provenientes de la codificación en el SPSS
1.2 Unificar data
Se juntan las 2 base de datos. Además se elimina data missing y se transforma los ítems a variables númericas (para fines de análisis).
data1 <- data1 %>%
select(Cedula, Sexo:Cadri35b) %>%
mutate(Colegio = "Colegio Independencia")
data2 <- data2 %>%
select(Cedula, Colegio:Cadri35b)
cadri_data <- rbind(data1, data2)
cadri_data <- cadri_data %>%
drop_na(Cadri1a:Cadri35b)
cadri_data <- cadri_data %>%
mutate(across(Cadri1a:Cadri35b, as.numeric))1.3 Descriptivos
Estadísticos descriptivos para la parte “a” de los ítems (perpetrador)
descriptivos_a <- cadri_data %>%
select(ends_with("a") & starts_with("Cadri")) %>%
psych::describe() %>%
mutate_if(is.numeric, round, 3)Estadísticos descriptivos para la parte “b” de los ítems (violencia sufrida)
descriptivos_b <- cadri_data %>%
select(ends_with("b") & starts_with("Cadri")) %>%
psych::describe() %>%
mutate_if(is.numeric, round, 3)1.3.1 Gráficos
Visualización de los descriptivos a y b
cadri_data %>%
select(ends_with("a") & starts_with("Cadri")) %>%
pivot_longer(
cols = everything(),
names_to = "Items",
values_to = "Puntaje"
) %>%
mutate(
Items = as_factor(Items),
Puntaje = factor(Puntaje,
levels = c(4, 3, 2, 1),
labels = c("6 o más veces",
"3 a 5 veces",
"1 a 2 veces",
"Nunca"))
) %>%
count(Items, Puntaje) %>%
ggplot(aes(x = Puntaje, y = n)) +
geom_col() +
coord_flip() +
facet_wrap(~ Items) +
labs(title = "Descriptivo de los ítems del CADRI - Violencia Cometida",
y = "",
x = "Participantes") +
theme_bw()cadri_data %>%
select(ends_with("b") & starts_with("Cadri")) %>%
pivot_longer(
cols = everything(),
names_to = "Items",
values_to = "Puntaje"
) %>%
mutate(
Items = as_factor(Items),
Puntaje = factor(Puntaje,
levels = c(4, 3, 2, 1),
labels = c("6 o más veces",
"3 a 5 veces",
"1 a 2 veces",
"Nunca"))
) %>%
count(Items, Puntaje) %>%
ggplot(aes(x = Puntaje, y = n)) +
geom_col() +
coord_flip() +
facet_wrap(~ Items) +
labs(title = "Descriptivo de los ítems del CADRI - Violencia Recibida",
y = "",
x = "Participantes") +
theme_bw()2 Análisis Factoriales Confirmatorios
La escala CADRI tiene 5 factores y se corresponden con los items de la siguiente manera:
- Violencia verbal: 4,7,9,12,17,21,23,24,28 y 32 = 10 items
- Violencia física: 8, 25, 30 y 34 = 4 items
- Conducta amenazante: 5, 29, 31 y 33 =4 items
- Violencia relacional: 3, 20, 35 = 3 items
- Violencia sexual: 2, 13, 15 y 19 = 4 items
- Items sobre solución de conflcitos: 1, 6, 10, 11, 14, 16, 18, 22, 26 y 27 =10 items
Nota: Todos los modelos tienen 2 análisis fit_a y fit_b que se diferencian únicamente por el estimador utilizado para su evaluación. El fit_a utiliza el estimador MLR que es ideal para data contínua y problemas de normalidad, mientras que el fit_b utiliza el estimador WLSMVque es el mejor estimador en consideración de la ordinalidad de los datos.
2.1 Modelo 01
El primer modelo probará todos los ítems tal cual se encuentran descritos en el instrumento original.
model01 <- "# Modelo de medición
V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri5a + Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri3a + Cadri20a + Cadri35a
V_sexual =~ Cadri2a + Cadri13a + Cadri15a + Cadri19a
R_confli =~ Cadri1a + Cadri6a + Cadri10a + Cadri11a + Cadri14a + Cadri16a + Cadri18a +
Cadri22a + Cadri26a + Cadri27a"2.1.1 Estimador MLR
lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 212 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 85
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 1586.734 1328.504
Degrees of freedom 545 545
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.194
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 4556.896 3264.743
Degrees of freedom 595 595
P-value 0.000 0.000
Scaling correction factor 1.396
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.737 0.707
Tucker-Lewis Index (TLI) 0.713 0.680
Robust Comparative Fit Index (CFI) 0.749
Robust Tucker-Lewis Index (TLI) 0.726
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -7788.654 -7788.654
Scaling correction factor 6.652
for the MLR correction
Loglikelihood unrestricted model (H1) -6995.286 -6995.286
Scaling correction factor 1.931
for the MLR correction
Akaike (AIC) 15747.307 15747.307
Bayesian (BIC) 16069.194 16069.194
Sample-size adjusted Bayesian (BIC) 15799.579 15799.579
Root Mean Square Error of Approximation:
RMSEA 0.077 0.066
90 Percent confidence interval - lower 0.072 0.062
90 Percent confidence interval - upper 0.081 0.071
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA 0.073
90 Percent confidence interval - lower 0.068
90 Percent confidence interval - upper 0.078
Standardized Root Mean Square Residual:
SRMR 0.083 0.083
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.370 0.451
Cadri7a 1.503 0.250 6.000 0.000 0.556 0.720
Cadri9a 1.141 0.174 6.550 0.000 0.422 0.549
Cadri12a 1.230 0.257 4.784 0.000 0.455 0.646
Cadri17a 1.075 0.214 5.025 0.000 0.398 0.696
Cadri21a 0.627 0.188 3.334 0.001 0.232 0.477
Cadri23a 0.377 0.097 3.870 0.000 0.139 0.330
Cadri24a 0.938 0.200 4.694 0.000 0.347 0.603
Cadri28a 1.352 0.230 5.885 0.000 0.500 0.633
Cadri32a 1.252 0.235 5.320 0.000 0.463 0.667
V_fisica =~
Cadri8a 1.000 0.268 0.720
Cadri25a 0.780 0.245 3.178 0.001 0.209 0.586
Cadri30a 1.103 0.241 4.567 0.000 0.296 0.694
Cadri34a 0.847 0.207 4.085 0.000 0.227 0.624
C_amenaz =~
Cadri5a 1.000 0.061 0.224
Cadri29a 1.697 1.425 1.191 0.234 0.103 0.268
Cadri31a 1.412 0.887 1.592 0.111 0.086 0.390
Cadri33a 2.609 1.601 1.629 0.103 0.159 0.592
V_relaci =~
Cadri3a 1.000 0.116 0.295
Cadri20a 1.690 1.082 1.562 0.118 0.195 0.575
Cadri35a 1.772 1.343 1.319 0.187 0.205 0.523
V_sexual =~
Cadri2a 1.000 0.166 0.368
Cadri13a 0.490 0.593 0.826 0.409 0.081 0.469
Cadri15a 0.530 0.564 0.938 0.348 0.088 0.652
Cadri19a 1.779 0.991 1.795 0.073 0.296 0.381
R_confli =~
Cadri1a 1.000 0.663 0.633
Cadri6a 0.887 0.074 12.048 0.000 0.588 0.644
Cadri10a 0.968 0.089 10.903 0.000 0.642 0.669
Cadri11a 0.909 0.075 12.047 0.000 0.603 0.664
Cadri14a 1.171 0.098 11.906 0.000 0.777 0.759
Cadri16a 1.094 0.101 10.824 0.000 0.726 0.720
Cadri18a 1.152 0.102 11.343 0.000 0.764 0.735
Cadri22a 1.014 0.107 9.472 0.000 0.672 0.639
Cadri26a 1.032 0.100 10.327 0.000 0.684 0.689
Cadri27a 0.732 0.090 8.138 0.000 0.486 0.512
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.061 0.021 2.925 0.003 0.617 0.617
C_amenaz 0.016 0.008 1.952 0.051 0.709 0.709
V_relaci 0.033 0.022 1.498 0.134 0.774 0.774
V_sexual 0.025 0.025 0.984 0.325 0.406 0.406
R_confli 0.098 0.023 4.188 0.000 0.401 0.401
V_fisica ~~
C_amenaz 0.017 0.008 2.058 0.040 1.014 1.014
V_relaci 0.009 0.011 0.801 0.423 0.287 0.287
V_sexual 0.022 0.024 0.938 0.348 0.498 0.498
R_confli 0.026 0.010 2.483 0.013 0.144 0.144
C_amenaz ~~
V_relaci 0.005 0.006 0.815 0.415 0.712 0.712
V_sexual 0.003 0.006 0.473 0.636 0.289 0.289
R_confli 0.007 0.007 1.056 0.291 0.171 0.171
V_relaci ~~
V_sexual 0.003 0.009 0.386 0.700 0.176 0.176
R_confli 0.015 0.011 1.404 0.160 0.195 0.195
V_sexual ~~
R_confli 0.023 0.030 0.745 0.456 0.205 0.205
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.537 0.057 9.407 0.000 0.537 0.797
.Cadri7a 0.287 0.032 8.857 0.000 0.287 0.481
.Cadri9a 0.413 0.048 8.535 0.000 0.413 0.699
.Cadri12a 0.288 0.042 6.809 0.000 0.288 0.582
.Cadri17a 0.168 0.023 7.336 0.000 0.168 0.516
.Cadri21a 0.183 0.034 5.367 0.000 0.183 0.773
.Cadri23a 0.159 0.041 3.907 0.000 0.159 0.891
.Cadri24a 0.210 0.023 9.108 0.000 0.210 0.636
.Cadri28a 0.373 0.042 8.906 0.000 0.373 0.599
.Cadri32a 0.267 0.038 7.105 0.000 0.267 0.555
.Cadri8a 0.067 0.015 4.582 0.000 0.067 0.481
.Cadri25a 0.084 0.021 3.904 0.000 0.084 0.657
.Cadri30a 0.094 0.033 2.877 0.004 0.094 0.519
.Cadri34a 0.081 0.024 3.350 0.001 0.081 0.611
.Cadri5a 0.070 0.031 2.300 0.021 0.070 0.950
.Cadri29a 0.138 0.027 5.131 0.000 0.138 0.928
.Cadri31a 0.041 0.025 1.617 0.106 0.041 0.848
.Cadri33a 0.047 0.025 1.855 0.064 0.047 0.649
.Cadri3a 0.140 0.045 3.087 0.002 0.140 0.913
.Cadri20a 0.077 0.031 2.462 0.014 0.077 0.669
.Cadri35a 0.111 0.031 3.569 0.000 0.111 0.726
.Cadri2a 0.176 0.056 3.118 0.002 0.176 0.864
.Cadri13a 0.023 0.010 2.337 0.019 0.023 0.780
.Cadri15a 0.011 0.011 0.991 0.322 0.011 0.576
.Cadri19a 0.513 0.154 3.326 0.001 0.513 0.855
.Cadri1a 0.657 0.058 11.270 0.000 0.657 0.599
.Cadri6a 0.489 0.048 10.089 0.000 0.489 0.586
.Cadri10a 0.509 0.051 9.926 0.000 0.509 0.553
.Cadri11a 0.461 0.048 9.579 0.000 0.461 0.559
.Cadri14a 0.444 0.050 8.848 0.000 0.444 0.424
.Cadri16a 0.489 0.055 8.859 0.000 0.489 0.482
.Cadri18a 0.497 0.053 9.427 0.000 0.497 0.460
.Cadri22a 0.654 0.067 9.756 0.000 0.654 0.591
.Cadri26a 0.517 0.056 9.202 0.000 0.517 0.525
.Cadri27a 0.663 0.053 12.462 0.000 0.663 0.738
V_verbal 0.137 0.041 3.346 0.001 1.000 1.000
V_fisica 0.072 0.032 2.231 0.026 1.000 1.000
C_amenaz 0.004 0.004 0.964 0.335 1.000 1.000
V_relaci 0.013 0.016 0.847 0.397 1.000 1.000
V_sexual 0.028 0.026 1.068 0.285 1.000 1.000
R_confli 0.440 0.065 6.791 0.000 1.000 1.0002.1.2 Estimador WLSMV
Warning message:
In readChar(file, size, TRUE) : truncating string with embedded nulsfit01_b <- cfa(model = model01,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -4.850446e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan WARNING: some estimated ov variances are negativelavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 61 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 151
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 1256.588 938.927
Degrees of freedom 545 545
P-value (Chi-square) 0.000 0.000
Scaling correction factor 2.187
Shift parameter 364.337
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 19668.362 6040.324
Degrees of freedom 595 595
P-value 0.000 0.000
Scaling correction factor 3.503
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.963 0.928
Tucker-Lewis Index (TLI) 0.959 0.921
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.063 0.047
90 Percent confidence interval - lower 0.059 0.042
90 Percent confidence interval - upper 0.068 0.052
P-value RMSEA <= 0.05 0.000 0.819
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.190 0.190
Weighted Root Mean Square Residual:
WRMR 1.344 1.344
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.602 0.602
Cadri7a 1.288 0.105 12.273 0.000 0.776 0.776
Cadri9a 1.123 0.101 11.078 0.000 0.676 0.676
Cadri12a 1.187 0.111 10.669 0.000 0.715 0.715
Cadri17a 1.309 0.116 11.327 0.000 0.788 0.788
Cadri21a 1.006 0.123 8.149 0.000 0.606 0.606
Cadri23a 0.870 0.122 7.147 0.000 0.524 0.524
Cadri24a 1.171 0.095 12.350 0.000 0.705 0.705
Cadri28a 1.229 0.107 11.471 0.000 0.740 0.740
Cadri32a 1.128 0.098 11.458 0.000 0.680 0.680
V_fisica =~
Cadri8a 1.000 0.839 0.839
Cadri25a 0.981 0.082 12.005 0.000 0.823 0.823
Cadri30a 1.014 0.088 11.535 0.000 0.851 0.851
Cadri34a 0.972 0.081 12.044 0.000 0.816 0.816
C_amenaz =~
Cadri5a 1.000 0.546 0.546
Cadri29a 1.120 0.174 6.445 0.000 0.611 0.611
Cadri31a 1.256 0.238 5.281 0.000 0.686 0.686
Cadri33a 1.589 0.241 6.584 0.000 0.868 0.868
V_relaci =~
Cadri3a 1.000 0.610 0.610
Cadri20a 1.323 0.225 5.888 0.000 0.807 0.807
Cadri35a 1.135 0.186 6.093 0.000 0.693 0.693
V_sexual =~
Cadri2a 1.000 0.629 0.629
Cadri13a 0.753 0.154 4.897 0.000 0.474 0.474
Cadri15a 1.872 0.339 5.524 0.000 1.178 1.178
Cadri19a 1.201 0.226 5.309 0.000 0.756 0.756
R_confli =~
Cadri1a 1.000 0.704 0.704
Cadri6a 1.043 0.056 18.680 0.000 0.734 0.734
Cadri10a 1.073 0.061 17.616 0.000 0.755 0.755
Cadri11a 1.021 0.059 17.300 0.000 0.719 0.719
Cadri14a 1.135 0.062 18.348 0.000 0.799 0.799
Cadri16a 1.166 0.058 20.261 0.000 0.821 0.821
Cadri18a 1.086 0.063 17.342 0.000 0.765 0.765
Cadri22a 1.068 0.064 16.764 0.000 0.752 0.752
Cadri26a 1.105 0.055 19.926 0.000 0.778 0.778
Cadri27a 0.839 0.063 13.378 0.000 0.591 0.591
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.375 0.045 8.322 0.000 0.741 0.741
C_amenaz 0.300 0.054 5.515 0.000 0.913 0.913
V_relaci 0.316 0.058 5.447 0.000 0.860 0.860
V_sexual 0.158 0.036 4.391 0.000 0.416 0.416
R_confli 0.220 0.030 7.420 0.000 0.518 0.518
V_fisica ~~
C_amenaz 0.464 0.073 6.351 0.000 1.011 1.011
V_relaci 0.253 0.075 3.371 0.001 0.493 0.493
V_sexual 0.268 0.052 5.169 0.000 0.508 0.508
R_confli 0.152 0.041 3.728 0.000 0.257 0.257
C_amenaz ~~
V_relaci 0.269 0.061 4.430 0.000 0.808 0.808
V_sexual 0.265 0.056 4.707 0.000 0.772 0.772
R_confli 0.155 0.039 3.960 0.000 0.403 0.403
V_relaci ~~
V_sexual 0.242 0.062 3.937 0.000 0.631 0.631
R_confli 0.132 0.042 3.119 0.002 0.307 0.307
V_sexual ~~
R_confli 0.150 0.046 3.242 0.001 0.339 0.339
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.000 0.000 0.000
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri23a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri5a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri3a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri13a 0.000 0.000 0.000
.Cadri15a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
.Cadri1a 0.000 0.000 0.000
.Cadri6a 0.000 0.000 0.000
.Cadri10a 0.000 0.000 0.000
.Cadri11a 0.000 0.000 0.000
.Cadri14a 0.000 0.000 0.000
.Cadri16a 0.000 0.000 0.000
.Cadri18a 0.000 0.000 0.000
.Cadri22a 0.000 0.000 0.000
.Cadri26a 0.000 0.000 0.000
.Cadri27a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fisica 0.000 0.000 0.000
C_amenaz 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
R_confli 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a|t1 -0.069 0.070 -0.994 0.320 -0.069 -0.069
Cadri4a|t2 1.035 0.085 12.173 0.000 1.035 1.035
Cadri4a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri23a|t1 1.224 0.092 13.258 0.000 1.224 1.224
Cadri23a|t2 2.088 0.166 12.614 0.000 2.088 2.088
Cadri23a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri5a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri5a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri5a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri3a|t1 1.472 0.105 13.974 0.000 1.472 1.472
Cadri3a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri3a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri13a|t1 2.024 0.157 12.925 0.000 2.024 2.024
Cadri13a|t2 2.740 0.329 8.326 0.000 2.740 2.740
Cadri15a|t1 2.357 0.214 11.014 0.000 2.357 2.357
Cadri15a|t2 2.740 0.329 8.326 0.000 2.740 2.740
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Cadri1a|t1 -0.729 0.077 -9.489 0.000 -0.729 -0.729
Cadri1a|t2 0.321 0.071 4.520 0.000 0.321 0.321
Cadri1a|t3 0.833 0.079 10.522 0.000 0.833 0.833
Cadri6a|t1 -0.632 0.075 -8.431 0.000 -0.632 -0.632
Cadri6a|t2 0.514 0.073 7.032 0.000 0.514 0.514
Cadri6a|t3 1.310 0.096 13.596 0.000 1.310 1.310
Cadri10a|t1 -0.889 0.081 -11.024 0.000 -0.889 -0.889
Cadri10a|t2 0.313 0.071 4.410 0.000 0.313 0.313
Cadri10a|t3 0.996 0.084 11.895 0.000 0.996 0.996
Cadri11a|t1 -0.935 0.082 -11.418 0.000 -0.935 -0.935
Cadri11a|t2 0.403 0.072 5.616 0.000 0.403 0.403
Cadri11a|t3 1.117 0.088 12.701 0.000 1.117 1.117
Cadri14a|t1 -0.935 0.082 -11.418 0.000 -0.935 -0.935
Cadri14a|t2 0.108 0.070 1.546 0.122 0.108 0.108
Cadri14a|t3 0.759 0.077 9.802 0.000 0.759 0.759
Cadri16a|t1 -0.549 0.074 -7.465 0.000 -0.549 -0.549
Cadri16a|t2 0.462 0.072 6.380 0.000 0.462 0.462
Cadri16a|t3 1.062 0.086 12.353 0.000 1.062 1.062
Cadri18a|t1 -1.035 0.085 -12.173 0.000 -1.035 -1.035
Cadri18a|t2 -0.062 0.070 -0.883 0.377 -0.062 -0.062
Cadri18a|t3 0.595 0.074 8.003 0.000 0.595 0.595
Cadri22a|t1 -0.749 0.077 -9.698 0.000 -0.749 -0.749
Cadri22a|t2 0.178 0.070 2.539 0.011 0.178 0.178
Cadri22a|t3 0.822 0.079 10.420 0.000 0.822 0.822
Cadri26a|t1 -0.699 0.076 -9.174 0.000 -0.699 -0.699
Cadri26a|t2 0.420 0.072 5.834 0.000 0.420 0.420
Cadri26a|t3 1.009 0.084 11.989 0.000 1.009 1.009
Cadri27a|t1 -0.241 0.070 -3.420 0.001 -0.241 -0.241
Cadri27a|t2 0.699 0.076 9.174 0.000 0.699 0.699
Cadri27a|t3 1.367 0.099 13.767 0.000 1.367 1.367
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.637 0.637 0.637
.Cadri7a 0.398 0.398 0.398
.Cadri9a 0.543 0.543 0.543
.Cadri12a 0.489 0.489 0.489
.Cadri17a 0.379 0.379 0.379
.Cadri21a 0.633 0.633 0.633
.Cadri23a 0.726 0.726 0.726
.Cadri24a 0.503 0.503 0.503
.Cadri28a 0.452 0.452 0.452
.Cadri32a 0.538 0.538 0.538
.Cadri8a 0.295 0.295 0.295
.Cadri25a 0.322 0.322 0.322
.Cadri30a 0.275 0.275 0.275
.Cadri34a 0.334 0.334 0.334
.Cadri5a 0.702 0.702 0.702
.Cadri29a 0.626 0.626 0.626
.Cadri31a 0.529 0.529 0.529
.Cadri33a 0.247 0.247 0.247
.Cadri3a 0.627 0.627 0.627
.Cadri20a 0.348 0.348 0.348
.Cadri35a 0.520 0.520 0.520
.Cadri2a 0.604 0.604 0.604
.Cadri13a 0.775 0.775 0.775
.Cadri15a -0.388 -0.388 -0.388
.Cadri19a 0.429 0.429 0.429
.Cadri1a 0.504 0.504 0.504
.Cadri6a 0.461 0.461 0.461
.Cadri10a 0.429 0.429 0.429
.Cadri11a 0.483 0.483 0.483
.Cadri14a 0.362 0.362 0.362
.Cadri16a 0.326 0.326 0.326
.Cadri18a 0.415 0.415 0.415
.Cadri22a 0.434 0.434 0.434
.Cadri26a 0.394 0.394 0.394
.Cadri27a 0.651 0.651 0.651
V_verbal 0.363 0.057 6.336 0.000 1.000 1.000
V_fisica 0.705 0.078 9.050 0.000 1.000 1.000
C_amenaz 0.298 0.083 3.576 0.000 1.000 1.000
V_relaci 0.373 0.101 3.679 0.000 1.000 1.000
V_sexual 0.396 0.116 3.404 0.001 1.000 1.000
R_confli 0.496 0.048 10.287 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a 1.000 1.000 1.000
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri23a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri5a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri3a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri13a 1.000 1.000 1.000
Cadri15a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.000
Cadri1a 1.000 1.000 1.000
Cadri6a 1.000 1.000 1.000
Cadri10a 1.000 1.000 1.000
Cadri11a 1.000 1.000 1.000
Cadri14a 1.000 1.000 1.000
Cadri16a 1.000 1.000 1.000
Cadri18a 1.000 1.000 1.000
Cadri22a 1.000 1.000 1.000
Cadri26a 1.000 1.000 1.000
Cadri27a 1.000 1.000 1.0002.1.3 Índices de Modificación (fit_b)
lavaan WARNING: starting values imply a correlation larger than 1;
variables involved are: V_fisica C_amenaz 2.1.4 Gráfico
## Graficar
semPlot::semPaths(fit01_b, whatLabels = "std", label.cex= 1.2,
edge.label.cex = 0.5, nCharEdges = 3,
nCharNodes = 8, sizeLat = 6, sizeLat2 = 4,
sizeMan = 6, sizeMan2 = 1.5, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 12, 2, 12), residScale = 10,
manifests = rev(fit01_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 01")
2.2 Modelo 02
A partir del análisis descriptivo y la observación del modelo 01, se hacen los siguientes cambios:
- Sin item 15, 13 (V Sexual)
- Sin item 05 (Conducta amenzante)
model02 <- "# Modelo de medición
V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri3a + Cadri20a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a
R_confli =~ Cadri1a + Cadri6a + Cadri10a + Cadri11a + Cadri14a + Cadri16a + Cadri18a +
Cadri22a + Cadri26a + Cadri27a"2.2.1 Estimador MLR
lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 262 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 79
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 1349.081 1060.264
Degrees of freedom 449 449
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.272
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 4244.153 2989.537
Degrees of freedom 496 496
P-value 0.000 0.000
Scaling correction factor 1.420
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.760 0.755
Tucker-Lewis Index (TLI) 0.735 0.729
Robust Comparative Fit Index (CFI) 0.780
Robust Tucker-Lewis Index (TLI) 0.757
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -8086.670 -8086.670
Scaling correction factor 4.767
for the MLR correction
Loglikelihood unrestricted model (H1) -7412.129 -7412.129
Scaling correction factor 1.795
for the MLR correction
Akaike (AIC) 16331.340 16331.340
Bayesian (BIC) 16630.505 16630.505
Sample-size adjusted Bayesian (BIC) 16379.922 16379.922
Root Mean Square Error of Approximation:
RMSEA 0.078 0.065
90 Percent confidence interval - lower 0.074 0.060
90 Percent confidence interval - upper 0.083 0.069
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA 0.073
90 Percent confidence interval - lower 0.067
90 Percent confidence interval - upper 0.079
Standardized Root Mean Square Residual:
SRMR 0.082 0.082
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.371 0.452
Cadri7a 1.503 0.250 6.012 0.000 0.558 0.723
Cadri9a 1.137 0.174 6.517 0.000 0.422 0.549
Cadri12a 1.229 0.257 4.775 0.000 0.456 0.648
Cadri17a 1.067 0.213 5.008 0.000 0.396 0.693
Cadri21a 0.621 0.188 3.292 0.001 0.230 0.474
Cadri23a 0.371 0.094 3.959 0.000 0.138 0.327
Cadri24a 0.932 0.196 4.751 0.000 0.346 0.601
Cadri28a 1.346 0.228 5.913 0.000 0.500 0.633
Cadri32a 1.251 0.233 5.360 0.000 0.464 0.669
V_fisica =~
Cadri8a 1.000 0.261 0.700
Cadri25a 0.764 0.236 3.234 0.001 0.199 0.558
Cadri30a 1.194 0.278 4.291 0.000 0.311 0.730
Cadri34a 0.881 0.218 4.045 0.000 0.230 0.631
C_amenaz =~
Cadri29a 1.000 0.112 0.291
Cadri31a 0.766 0.731 1.047 0.295 0.086 0.390
Cadri33a 1.325 1.173 1.130 0.259 0.149 0.555
V_relaci =~
Cadri3a 1.000 0.127 0.323
Cadri20a 1.489 1.020 1.461 0.144 0.188 0.554
Cadri35a 1.601 1.273 1.258 0.209 0.203 0.517
V_sexual =~
Cadri2a 1.000 0.211 0.467
Cadri19a 2.021 1.535 1.317 0.188 0.426 0.549
R_confli =~
Cadri1a 1.000 0.663 0.633
Cadri6a 0.888 0.074 12.055 0.000 0.589 0.644
Cadri10a 0.965 0.089 10.888 0.000 0.640 0.667
Cadri11a 0.909 0.075 12.051 0.000 0.603 0.664
Cadri14a 1.171 0.098 11.899 0.000 0.776 0.758
Cadri16a 1.097 0.101 10.816 0.000 0.727 0.722
Cadri18a 1.153 0.101 11.358 0.000 0.764 0.735
Cadri22a 1.012 0.107 9.435 0.000 0.671 0.637
Cadri26a 1.033 0.100 10.343 0.000 0.685 0.690
Cadri27a 0.735 0.090 8.142 0.000 0.487 0.514
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.059 0.021 2.829 0.005 0.611 0.611
C_amenaz 0.030 0.021 1.416 0.157 0.724 0.724
V_relaci 0.037 0.024 1.509 0.131 0.784 0.784
V_sexual 0.035 0.017 2.058 0.040 0.441 0.441
R_confli 0.099 0.024 4.188 0.000 0.401 0.401
V_fisica ~~
C_amenaz 0.031 0.018 1.733 0.083 1.052 1.052
V_relaci 0.010 0.013 0.792 0.428 0.307 0.307
V_sexual 0.027 0.011 2.362 0.018 0.491 0.491
R_confli 0.025 0.010 2.476 0.013 0.146 0.146
C_amenaz ~~
V_relaci 0.011 0.014 0.776 0.438 0.781 0.781
V_sexual 0.013 0.016 0.791 0.429 0.538 0.538
R_confli 0.014 0.018 0.783 0.433 0.190 0.190
V_relaci ~~
V_sexual 0.015 0.019 0.781 0.435 0.549 0.549
R_confli 0.017 0.012 1.353 0.176 0.201 0.201
V_sexual ~~
R_confli 0.045 0.024 1.890 0.059 0.326 0.326
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.536 0.057 9.413 0.000 0.536 0.795
.Cadri7a 0.284 0.032 8.765 0.000 0.284 0.477
.Cadri9a 0.413 0.049 8.512 0.000 0.413 0.699
.Cadri12a 0.287 0.042 6.793 0.000 0.287 0.580
.Cadri17a 0.169 0.023 7.350 0.000 0.169 0.519
.Cadri21a 0.183 0.034 5.389 0.000 0.183 0.775
.Cadri23a 0.159 0.041 3.909 0.000 0.159 0.893
.Cadri24a 0.211 0.023 9.083 0.000 0.211 0.638
.Cadri28a 0.374 0.042 8.955 0.000 0.374 0.600
.Cadri32a 0.266 0.037 7.112 0.000 0.266 0.552
.Cadri8a 0.071 0.015 4.739 0.000 0.071 0.510
.Cadri25a 0.088 0.020 4.290 0.000 0.088 0.689
.Cadri30a 0.085 0.030 2.881 0.004 0.085 0.468
.Cadri34a 0.080 0.023 3.468 0.001 0.080 0.602
.Cadri29a 0.136 0.026 5.263 0.000 0.136 0.915
.Cadri31a 0.041 0.024 1.683 0.092 0.041 0.848
.Cadri33a 0.050 0.025 1.974 0.048 0.050 0.692
.Cadri3a 0.138 0.043 3.172 0.002 0.138 0.896
.Cadri20a 0.080 0.032 2.494 0.013 0.080 0.693
.Cadri35a 0.112 0.031 3.636 0.000 0.112 0.733
.Cadri2a 0.159 0.040 3.950 0.000 0.159 0.782
.Cadri19a 0.420 0.150 2.801 0.005 0.420 0.698
.Cadri1a 0.657 0.058 11.294 0.000 0.657 0.599
.Cadri6a 0.488 0.048 10.080 0.000 0.488 0.585
.Cadri10a 0.511 0.051 9.935 0.000 0.511 0.555
.Cadri11a 0.460 0.048 9.562 0.000 0.460 0.559
.Cadri14a 0.445 0.050 8.836 0.000 0.445 0.425
.Cadri16a 0.487 0.055 8.854 0.000 0.487 0.479
.Cadri18a 0.497 0.053 9.392 0.000 0.497 0.460
.Cadri22a 0.657 0.068 9.715 0.000 0.657 0.594
.Cadri26a 0.517 0.056 9.215 0.000 0.517 0.524
.Cadri27a 0.662 0.053 12.476 0.000 0.662 0.736
V_verbal 0.138 0.041 3.360 0.001 1.000 1.000
V_fisica 0.068 0.032 2.142 0.032 1.000 1.000
C_amenaz 0.013 0.011 1.096 0.273 1.000 1.000
V_relaci 0.016 0.019 0.858 0.391 1.000 1.000
V_sexual 0.044 0.037 1.207 0.228 1.000 1.000
R_confli 0.439 0.065 6.797 0.000 1.000 1.000lavaan WARNING: starting values imply a correlation larger than 1;
variables involved are: V_fisica C_amenaz 2.2.2 Estimador WLSMV
fit02_b <- cfa(model = model02,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -5.562418e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 67 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 141
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 1036.245 843.596
Degrees of freedom 449 449
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.823
Shift parameter 275.200
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 18715.935 6266.895
Degrees of freedom 496 496
P-value 0.000 0.000
Scaling correction factor 3.157
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.968 0.932
Tucker-Lewis Index (TLI) 0.964 0.924
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.063 0.052
90 Percent confidence interval - lower 0.058 0.047
90 Percent confidence interval - upper 0.069 0.057
P-value RMSEA <= 0.05 0.000 0.266
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.119 0.119
Weighted Root Mean Square Residual:
WRMR 1.325 1.325
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.603 0.603
Cadri7a 1.288 0.106 12.157 0.000 0.776 0.776
Cadri9a 1.123 0.103 10.928 0.000 0.677 0.677
Cadri12a 1.178 0.111 10.625 0.000 0.710 0.710
Cadri17a 1.305 0.115 11.335 0.000 0.787 0.787
Cadri21a 0.998 0.123 8.083 0.000 0.602 0.602
Cadri23a 0.859 0.121 7.077 0.000 0.518 0.518
Cadri24a 1.164 0.094 12.325 0.000 0.702 0.702
Cadri28a 1.203 0.107 11.294 0.000 0.725 0.725
Cadri32a 1.178 0.103 11.468 0.000 0.710 0.710
V_fisica =~
Cadri8a 1.000 0.832 0.832
Cadri25a 0.945 0.080 11.816 0.000 0.787 0.787
Cadri30a 1.069 0.091 11.716 0.000 0.889 0.889
Cadri34a 0.985 0.082 12.063 0.000 0.819 0.819
C_amenaz =~
Cadri29a 1.000 0.608 0.608
Cadri31a 1.141 0.185 6.174 0.000 0.694 0.694
Cadri33a 1.428 0.192 7.425 0.000 0.869 0.869
V_relaci =~
Cadri3a 1.000 0.620 0.620
Cadri20a 1.291 0.217 5.939 0.000 0.800 0.800
Cadri35a 1.117 0.181 6.163 0.000 0.692 0.692
V_sexual =~
Cadri2a 1.000 0.596 0.596
Cadri19a 1.330 0.289 4.605 0.000 0.793 0.793
R_confli =~
Cadri1a 1.000 0.702 0.702
Cadri6a 1.045 0.056 18.606 0.000 0.733 0.733
Cadri10a 1.075 0.061 17.532 0.000 0.754 0.754
Cadri11a 1.027 0.059 17.295 0.000 0.721 0.721
Cadri14a 1.141 0.062 18.269 0.000 0.800 0.800
Cadri16a 1.170 0.058 20.166 0.000 0.821 0.821
Cadri18a 1.092 0.063 17.295 0.000 0.766 0.766
Cadri22a 1.072 0.064 16.684 0.000 0.752 0.752
Cadri26a 1.108 0.056 19.879 0.000 0.778 0.778
Cadri27a 0.841 0.063 13.294 0.000 0.590 0.590
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.371 0.045 8.266 0.000 0.740 0.740
C_amenaz 0.328 0.043 7.694 0.000 0.895 0.895
V_relaci 0.321 0.057 5.599 0.000 0.859 0.859
V_sexual 0.168 0.045 3.782 0.000 0.469 0.469
R_confli 0.219 0.029 7.424 0.000 0.518 0.518
V_fisica ~~
C_amenaz 0.509 0.065 7.824 0.000 1.004 1.004
V_relaci 0.255 0.074 3.442 0.001 0.495 0.495
V_sexual 0.267 0.063 4.238 0.000 0.538 0.538
R_confli 0.150 0.040 3.759 0.000 0.258 0.258
C_amenaz ~~
V_relaci 0.303 0.058 5.229 0.000 0.804 0.804
V_sexual 0.275 0.069 4.001 0.000 0.759 0.759
R_confli 0.176 0.036 4.912 0.000 0.411 0.411
V_relaci ~~
V_sexual 0.223 0.060 3.722 0.000 0.605 0.605
R_confli 0.134 0.042 3.159 0.002 0.307 0.307
V_sexual ~~
R_confli 0.146 0.043 3.418 0.001 0.350 0.350
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.000 0.000 0.000
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri23a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri3a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
.Cadri1a 0.000 0.000 0.000
.Cadri6a 0.000 0.000 0.000
.Cadri10a 0.000 0.000 0.000
.Cadri11a 0.000 0.000 0.000
.Cadri14a 0.000 0.000 0.000
.Cadri16a 0.000 0.000 0.000
.Cadri18a 0.000 0.000 0.000
.Cadri22a 0.000 0.000 0.000
.Cadri26a 0.000 0.000 0.000
.Cadri27a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fisica 0.000 0.000 0.000
C_amenaz 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
R_confli 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a|t1 -0.069 0.070 -0.994 0.320 -0.069 -0.069
Cadri4a|t2 1.035 0.085 12.173 0.000 1.035 1.035
Cadri4a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri23a|t1 1.224 0.092 13.258 0.000 1.224 1.224
Cadri23a|t2 2.088 0.166 12.614 0.000 2.088 2.088
Cadri23a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri3a|t1 1.472 0.105 13.974 0.000 1.472 1.472
Cadri3a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri3a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Cadri1a|t1 -0.729 0.077 -9.489 0.000 -0.729 -0.729
Cadri1a|t2 0.321 0.071 4.520 0.000 0.321 0.321
Cadri1a|t3 0.833 0.079 10.522 0.000 0.833 0.833
Cadri6a|t1 -0.632 0.075 -8.431 0.000 -0.632 -0.632
Cadri6a|t2 0.514 0.073 7.032 0.000 0.514 0.514
Cadri6a|t3 1.310 0.096 13.596 0.000 1.310 1.310
Cadri10a|t1 -0.889 0.081 -11.024 0.000 -0.889 -0.889
Cadri10a|t2 0.313 0.071 4.410 0.000 0.313 0.313
Cadri10a|t3 0.996 0.084 11.895 0.000 0.996 0.996
Cadri11a|t1 -0.935 0.082 -11.418 0.000 -0.935 -0.935
Cadri11a|t2 0.403 0.072 5.616 0.000 0.403 0.403
Cadri11a|t3 1.117 0.088 12.701 0.000 1.117 1.117
Cadri14a|t1 -0.935 0.082 -11.418 0.000 -0.935 -0.935
Cadri14a|t2 0.108 0.070 1.546 0.122 0.108 0.108
Cadri14a|t3 0.759 0.077 9.802 0.000 0.759 0.759
Cadri16a|t1 -0.549 0.074 -7.465 0.000 -0.549 -0.549
Cadri16a|t2 0.462 0.072 6.380 0.000 0.462 0.462
Cadri16a|t3 1.062 0.086 12.353 0.000 1.062 1.062
Cadri18a|t1 -1.035 0.085 -12.173 0.000 -1.035 -1.035
Cadri18a|t2 -0.062 0.070 -0.883 0.377 -0.062 -0.062
Cadri18a|t3 0.595 0.074 8.003 0.000 0.595 0.595
Cadri22a|t1 -0.749 0.077 -9.698 0.000 -0.749 -0.749
Cadri22a|t2 0.178 0.070 2.539 0.011 0.178 0.178
Cadri22a|t3 0.822 0.079 10.420 0.000 0.822 0.822
Cadri26a|t1 -0.699 0.076 -9.174 0.000 -0.699 -0.699
Cadri26a|t2 0.420 0.072 5.834 0.000 0.420 0.420
Cadri26a|t3 1.009 0.084 11.989 0.000 1.009 1.009
Cadri27a|t1 -0.241 0.070 -3.420 0.001 -0.241 -0.241
Cadri27a|t2 0.699 0.076 9.174 0.000 0.699 0.699
Cadri27a|t3 1.367 0.099 13.767 0.000 1.367 1.367
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.636 0.636 0.636
.Cadri7a 0.397 0.397 0.397
.Cadri9a 0.542 0.542 0.542
.Cadri12a 0.495 0.495 0.495
.Cadri17a 0.381 0.381 0.381
.Cadri21a 0.638 0.638 0.638
.Cadri23a 0.732 0.732 0.732
.Cadri24a 0.507 0.507 0.507
.Cadri28a 0.474 0.474 0.474
.Cadri32a 0.496 0.496 0.496
.Cadri8a 0.308 0.308 0.308
.Cadri25a 0.381 0.381 0.381
.Cadri30a 0.209 0.209 0.209
.Cadri34a 0.329 0.329 0.329
.Cadri29a 0.630 0.630 0.630
.Cadri31a 0.518 0.518 0.518
.Cadri33a 0.245 0.245 0.245
.Cadri3a 0.616 0.616 0.616
.Cadri20a 0.360 0.360 0.360
.Cadri35a 0.521 0.521 0.521
.Cadri2a 0.645 0.645 0.645
.Cadri19a 0.372 0.372 0.372
.Cadri1a 0.508 0.508 0.508
.Cadri6a 0.462 0.462 0.462
.Cadri10a 0.431 0.431 0.431
.Cadri11a 0.481 0.481 0.481
.Cadri14a 0.360 0.360 0.360
.Cadri16a 0.326 0.326 0.326
.Cadri18a 0.413 0.413 0.413
.Cadri22a 0.434 0.434 0.434
.Cadri26a 0.395 0.395 0.395
.Cadri27a 0.652 0.652 0.652
V_verbal 0.364 0.057 6.343 0.000 1.000 1.000
V_fisica 0.692 0.076 9.063 0.000 1.000 1.000
C_amenaz 0.370 0.073 5.062 0.000 1.000 1.000
V_relaci 0.384 0.102 3.772 0.000 1.000 1.000
V_sexual 0.355 0.106 3.345 0.001 1.000 1.000
R_confli 0.492 0.048 10.228 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a 1.000 1.000 1.000
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri23a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri3a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.000
Cadri1a 1.000 1.000 1.000
Cadri6a 1.000 1.000 1.000
Cadri10a 1.000 1.000 1.000
Cadri11a 1.000 1.000 1.000
Cadri14a 1.000 1.000 1.000
Cadri16a 1.000 1.000 1.000
Cadri18a 1.000 1.000 1.000
Cadri22a 1.000 1.000 1.000
Cadri26a 1.000 1.000 1.000
Cadri27a 1.000 1.000 1.0002.2.3 Gráfico
## Graficar
semPlot::semPaths(fit02_b, whatLabels = "std", label.cex= 1.2,
edge.label.cex = 0.5, nCharEdges = 3,
nCharNodes = 8, sizeLat = 6, sizeLat2 = 4,
sizeMan = 6, sizeMan2 = 1.5, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 12, 2, 12), residScale = 10,
manifests = rev(fit02_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 02")
2.3 Modelo 03
Se mantienen los cambios del Modelo 02: - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante)
Se adiciona el siguiente cambio: - No considerar al factor Resolución de conflictos como parte del modelo factorial
model03 <- "# Modelo de medición
V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri3a + Cadri20a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.3.1 Estimador MLR
lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 261 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 54
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 719.059 456.499
Degrees of freedom 199 199
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.575
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 2322.424 1250.743
Degrees of freedom 231 231
P-value 0.000 0.000
Scaling correction factor 1.857
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.751 0.747
Tucker-Lewis Index (TLI) 0.711 0.707
Robust Comparative Fit Index (CFI) 0.786
Robust Tucker-Lewis Index (TLI) 0.751
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4146.842 -4146.842
Scaling correction factor 6.547
for the MLR correction
Loglikelihood unrestricted model (H1) -3787.312 -3787.312
Scaling correction factor 2.636
for the MLR correction
Akaike (AIC) 8401.684 8401.684
Bayesian (BIC) 8606.177 8606.177
Sample-size adjusted Bayesian (BIC) 8434.892 8434.892
Root Mean Square Error of Approximation:
RMSEA 0.090 0.063
90 Percent confidence interval - lower 0.083 0.057
90 Percent confidence interval - upper 0.097 0.069
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA 0.079
90 Percent confidence interval - lower 0.070
90 Percent confidence interval - upper 0.089
Standardized Root Mean Square Residual:
SRMR 0.078 0.078
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.358 0.436
Cadri7a 1.545 0.263 5.875 0.000 0.553 0.716
Cadri9a 1.159 0.184 6.288 0.000 0.415 0.540
Cadri12a 1.294 0.271 4.772 0.000 0.463 0.658
Cadri17a 1.120 0.226 4.959 0.000 0.401 0.702
Cadri21a 0.658 0.197 3.336 0.001 0.235 0.484
Cadri23a 0.389 0.098 3.981 0.000 0.139 0.330
Cadri24a 0.950 0.207 4.589 0.000 0.340 0.591
Cadri28a 1.383 0.240 5.767 0.000 0.495 0.627
Cadri32a 1.320 0.243 5.427 0.000 0.472 0.681
V_fisica =~
Cadri8a 1.000 0.260 0.697
Cadri25a 0.767 0.236 3.247 0.001 0.199 0.557
Cadri30a 1.203 0.282 4.264 0.000 0.312 0.732
Cadri34a 0.888 0.220 4.029 0.000 0.231 0.633
C_amenaz =~
Cadri29a 1.000 0.112 0.292
Cadri31a 0.766 0.720 1.065 0.287 0.086 0.391
Cadri33a 1.323 1.123 1.177 0.239 0.148 0.554
V_relaci =~
Cadri3a 1.000 0.127 0.324
Cadri20a 1.472 1.017 1.448 0.148 0.187 0.550
Cadri35a 1.602 1.292 1.240 0.215 0.203 0.519
V_sexual =~
Cadri2a 1.000 0.213 0.471
Cadri19a 1.984 1.826 1.086 0.277 0.422 0.544
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.057 0.020 2.791 0.005 0.611 0.611
C_amenaz 0.029 0.020 1.488 0.137 0.724 0.724
V_relaci 0.036 0.024 1.484 0.138 0.784 0.784
V_sexual 0.033 0.018 1.878 0.060 0.440 0.440
V_fisica ~~
C_amenaz 0.031 0.017 1.803 0.071 1.052 1.052
V_relaci 0.010 0.013 0.786 0.432 0.309 0.309
V_sexual 0.027 0.012 2.270 0.023 0.491 0.491
C_amenaz ~~
V_relaci 0.011 0.014 0.789 0.430 0.784 0.784
V_sexual 0.013 0.016 0.796 0.426 0.538 0.538
V_relaci ~~
V_sexual 0.015 0.021 0.721 0.471 0.556 0.556
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.546 0.057 9.580 0.000 0.546 0.810
.Cadri7a 0.290 0.033 8.815 0.000 0.290 0.487
.Cadri9a 0.419 0.049 8.547 0.000 0.419 0.709
.Cadri12a 0.281 0.042 6.625 0.000 0.281 0.567
.Cadri17a 0.166 0.023 7.135 0.000 0.166 0.507
.Cadri21a 0.181 0.034 5.342 0.000 0.181 0.765
.Cadri23a 0.159 0.041 3.891 0.000 0.159 0.891
.Cadri24a 0.215 0.024 9.134 0.000 0.215 0.651
.Cadri28a 0.379 0.043 8.838 0.000 0.379 0.607
.Cadri32a 0.258 0.037 6.893 0.000 0.258 0.537
.Cadri8a 0.071 0.015 4.726 0.000 0.071 0.515
.Cadri25a 0.088 0.020 4.319 0.000 0.088 0.689
.Cadri30a 0.085 0.029 2.881 0.004 0.085 0.464
.Cadri34a 0.079 0.023 3.476 0.001 0.079 0.599
.Cadri29a 0.136 0.026 5.244 0.000 0.136 0.915
.Cadri31a 0.041 0.024 1.691 0.091 0.041 0.847
.Cadri33a 0.050 0.025 1.989 0.047 0.050 0.693
.Cadri3a 0.137 0.043 3.163 0.002 0.137 0.895
.Cadri20a 0.081 0.032 2.492 0.013 0.081 0.697
.Cadri35a 0.112 0.031 3.628 0.000 0.112 0.730
.Cadri2a 0.158 0.045 3.517 0.000 0.158 0.778
.Cadri19a 0.423 0.174 2.427 0.015 0.423 0.704
V_verbal 0.128 0.040 3.210 0.001 1.000 1.000
V_fisica 0.067 0.032 2.136 0.033 1.000 1.000
C_amenaz 0.013 0.011 1.153 0.249 1.000 1.000
V_relaci 0.016 0.019 0.849 0.396 1.000 1.000
V_sexual 0.045 0.044 1.037 0.300 1.000 1.000lavaan WARNING: starting values imply a correlation larger than 1;
variables involved are: V_fisica C_amenaz 2.3.2 Estimador WLSMV
fit03_b <- cfa(model = model03,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -6.588562e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 55 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 96
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 295.580 321.481
Degrees of freedom 199 199
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.346
Shift parameter 101.852
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 7432.070 2947.528
Degrees of freedom 231 231
P-value 0.000 0.000
Scaling correction factor 2.651
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.987 0.955
Tucker-Lewis Index (TLI) 0.984 0.948
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.039 0.044
90 Percent confidence interval - lower 0.029 0.035
90 Percent confidence interval - upper 0.048 0.052
P-value RMSEA <= 0.05 0.983 0.890
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.105 0.105
Weighted Root Mean Square Residual:
WRMR 1.001 1.001
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri4a 1.000 0.534 0.534
Cadri7a 1.410 0.131 10.752 0.000 0.754 0.754
Cadri9a 1.225 0.126 9.701 0.000 0.655 0.655
Cadri12a 1.386 0.145 9.547 0.000 0.741 0.741
Cadri17a 1.541 0.156 9.866 0.000 0.824 0.824
Cadri21a 1.250 0.152 8.231 0.000 0.668 0.668
Cadri23a 1.038 0.139 7.493 0.000 0.555 0.555
Cadri24a 1.247 0.119 10.485 0.000 0.667 0.667
Cadri28a 1.319 0.133 9.893 0.000 0.705 0.705
Cadri32a 1.389 0.136 10.216 0.000 0.743 0.743
V_fisica =~
Cadri8a 1.000 0.838 0.838
Cadri25a 0.938 0.076 12.332 0.000 0.786 0.786
Cadri30a 1.054 0.087 12.127 0.000 0.884 0.884
Cadri34a 0.977 0.077 12.686 0.000 0.819 0.819
C_amenaz =~
Cadri29a 1.000 0.574 0.574
Cadri31a 1.235 0.197 6.271 0.000 0.709 0.709
Cadri33a 1.553 0.220 7.054 0.000 0.892 0.892
V_relaci =~
Cadri3a 1.000 0.618 0.618
Cadri20a 1.309 0.211 6.201 0.000 0.809 0.809
Cadri35a 1.108 0.166 6.678 0.000 0.685 0.685
V_sexual =~
Cadri2a 1.000 0.600 0.600
Cadri19a 1.313 0.287 4.577 0.000 0.787 0.787
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.332 0.043 7.658 0.000 0.741 0.741
C_amenaz 0.272 0.039 6.880 0.000 0.885 0.885
V_relaci 0.284 0.055 5.174 0.000 0.860 0.860
V_sexual 0.152 0.040 3.828 0.000 0.475 0.475
V_fisica ~~
C_amenaz 0.477 0.061 7.804 0.000 0.991 0.991
V_relaci 0.257 0.075 3.430 0.001 0.495 0.495
V_sexual 0.271 0.062 4.402 0.000 0.540 0.540
C_amenaz ~~
V_relaci 0.282 0.055 5.164 0.000 0.796 0.796
V_sexual 0.259 0.066 3.930 0.000 0.753 0.753
V_relaci ~~
V_sexual 0.225 0.059 3.813 0.000 0.608 0.608
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.000 0.000 0.000
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri23a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri3a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fisica 0.000 0.000 0.000
C_amenaz 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a|t1 -0.069 0.070 -0.994 0.320 -0.069 -0.069
Cadri4a|t2 1.035 0.085 12.173 0.000 1.035 1.035
Cadri4a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri23a|t1 1.224 0.092 13.258 0.000 1.224 1.224
Cadri23a|t2 2.088 0.166 12.614 0.000 2.088 2.088
Cadri23a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri3a|t1 1.472 0.105 13.974 0.000 1.472 1.472
Cadri3a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri3a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri4a 0.714 0.714 0.714
.Cadri7a 0.432 0.432 0.432
.Cadri9a 0.572 0.572 0.572
.Cadri12a 0.451 0.451 0.451
.Cadri17a 0.321 0.321 0.321
.Cadri21a 0.554 0.554 0.554
.Cadri23a 0.692 0.692 0.692
.Cadri24a 0.556 0.556 0.556
.Cadri28a 0.503 0.503 0.503
.Cadri32a 0.449 0.449 0.449
.Cadri8a 0.297 0.297 0.297
.Cadri25a 0.382 0.382 0.382
.Cadri30a 0.219 0.219 0.219
.Cadri34a 0.329 0.329 0.329
.Cadri29a 0.670 0.670 0.670
.Cadri31a 0.497 0.497 0.497
.Cadri33a 0.205 0.205 0.205
.Cadri3a 0.618 0.618 0.618
.Cadri20a 0.346 0.346 0.346
.Cadri35a 0.531 0.531 0.531
.Cadri2a 0.640 0.640 0.640
.Cadri19a 0.380 0.380 0.380
V_verbal 0.286 0.054 5.324 0.000 1.000 1.000
V_fisica 0.703 0.074 9.472 0.000 1.000 1.000
C_amenaz 0.330 0.070 4.724 0.000 1.000 1.000
V_relaci 0.382 0.097 3.921 0.000 1.000 1.000
V_sexual 0.360 0.104 3.465 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri4a 1.000 1.000 1.000
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri23a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri3a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.3.3 Gráfico
## Graficar
semPlot::semPaths(fit03_b, whatLabels = "std", label.cex= 1.2,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.5, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 2, 15), residScale = 10,
manifests = rev(fit03_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 03")
2.3.4 Notas de análisis
Análisis del modelo 02 y 03
- Cargas factoriales menores a 0.45:
- Violencia verbal
- Item 04
- Item 23
- Conducta amenzante
- Item 05
- Item 29
- Item 33
- Violencia verbal
- Violencia relacional
- Item 03 (aparece un sinsentido en los índices de modificación)
Conducta amenzante (Correlación mayor a 1): Violencia verbal, física y amenzante
- Análisis de Índices de modificación:
- Resolución de conflictos: Correlación: Item 16 y 26
- Correlación interfactores: Item 8 (violencia física) y 33 (Conducta amenazante de violencia física)
- Item 23 no guarda relación con constructo. además está observada por baja carga factorial.
2.4 Modelo 04
Se mantienen los cambios del Modelo 02 y 03: - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante) - No considerar al factor Resolución de conflictos como parte del modelo factorial
Se adiciona el siguiente cambio: - Sin item 04 y 23 (V. Verbal) - Sin item 03 (V. relacional)
model04 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.4.1 Estimador MLR
lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 191 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 48
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 445.926 268.488
Degrees of freedom 142 142
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.661
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1937.884 987.012
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 1.963
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.828 0.845
Tucker-Lewis Index (TLI) 0.793 0.813
Robust Comparative Fit Index (CFI) 0.869
Robust Tucker-Lewis Index (TLI) 0.842
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3465.899 -3465.899
Scaling correction factor 6.492
for the MLR correction
Loglikelihood unrestricted model (H1) -3242.936 -3242.936
Scaling correction factor 2.881
for the MLR correction
Akaike (AIC) 7027.798 7027.798
Bayesian (BIC) 7209.570 7209.570
Sample-size adjusted Bayesian (BIC) 7057.317 7057.317
Root Mean Square Error of Approximation:
RMSEA 0.081 0.052
90 Percent confidence interval - lower 0.073 0.045
90 Percent confidence interval - upper 0.090 0.060
P-value RMSEA <= 0.05 0.000 0.300
Robust RMSEA 0.067
90 Percent confidence interval - lower 0.055
90 Percent confidence interval - upper 0.080
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.540 0.699
Cadri9a 0.755 0.103 7.346 0.000 0.407 0.530
Cadri12a 0.889 0.097 9.115 0.000 0.480 0.681
Cadri17a 0.756 0.084 9.026 0.000 0.408 0.715
Cadri21a 0.440 0.110 4.014 0.000 0.238 0.489
Cadri24a 0.636 0.099 6.427 0.000 0.343 0.597
Cadri28a 0.911 0.098 9.266 0.000 0.491 0.622
Cadri32a 0.869 0.091 9.525 0.000 0.469 0.676
V_fisica =~
Cadri8a 1.000 0.257 0.690
Cadri25a 0.760 0.231 3.283 0.001 0.195 0.547
Cadri30a 1.236 0.299 4.130 0.000 0.318 0.745
Cadri34a 0.894 0.226 3.951 0.000 0.230 0.631
C_amenaz =~
Cadri29a 1.000 0.109 0.283
Cadri31a 0.747 0.764 0.977 0.329 0.081 0.370
Cadri33a 1.407 1.247 1.128 0.259 0.154 0.573
V_relaci =~
Cadri20a 1.000 0.196 0.575
Cadri35a 1.098 0.410 2.680 0.007 0.215 0.548
V_sexual =~
Cadri2a 1.000 0.201 0.446
Cadri19a 2.218 2.076 1.069 0.285 0.446 0.576
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.086 0.032 2.696 0.007 0.620 0.620
C_amenaz 0.042 0.034 1.245 0.213 0.720 0.720
V_relaci 0.077 0.036 2.140 0.032 0.727 0.727
V_sexual 0.046 0.028 1.636 0.102 0.421 0.421
V_fisica ~~
C_amenaz 0.030 0.018 1.604 0.109 1.055 1.055
V_relaci 0.011 0.009 1.126 0.260 0.210 0.210
V_sexual 0.026 0.013 2.016 0.044 0.494 0.494
C_amenaz ~~
V_relaci 0.013 0.010 1.309 0.191 0.626 0.626
V_sexual 0.011 0.015 0.769 0.442 0.514 0.514
V_relaci ~~
V_sexual 0.017 0.021 0.783 0.434 0.422 0.422
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.305 0.036 8.460 0.000 0.305 0.511
.Cadri9a 0.425 0.049 8.604 0.000 0.425 0.719
.Cadri12a 0.266 0.042 6.395 0.000 0.266 0.536
.Cadri17a 0.160 0.023 6.804 0.000 0.160 0.489
.Cadri21a 0.180 0.034 5.260 0.000 0.180 0.761
.Cadri24a 0.213 0.024 8.882 0.000 0.213 0.644
.Cadri28a 0.382 0.044 8.705 0.000 0.382 0.613
.Cadri32a 0.262 0.039 6.789 0.000 0.262 0.543
.Cadri8a 0.073 0.015 4.694 0.000 0.073 0.524
.Cadri25a 0.089 0.020 4.409 0.000 0.089 0.701
.Cadri30a 0.081 0.028 2.923 0.003 0.081 0.445
.Cadri34a 0.080 0.023 3.542 0.000 0.080 0.602
.Cadri29a 0.136 0.026 5.215 0.000 0.136 0.920
.Cadri31a 0.042 0.026 1.618 0.106 0.042 0.863
.Cadri33a 0.048 0.026 1.865 0.062 0.048 0.671
.Cadri20a 0.077 0.030 2.580 0.010 0.077 0.669
.Cadri35a 0.107 0.030 3.629 0.000 0.107 0.700
.Cadri2a 0.163 0.045 3.590 0.000 0.163 0.801
.Cadri19a 0.402 0.197 2.035 0.042 0.402 0.669
V_verbal 0.291 0.059 4.960 0.000 1.000 1.000
V_fisica 0.066 0.031 2.101 0.036 1.000 1.000
C_amenaz 0.012 0.011 1.080 0.280 1.000 1.000
V_relaci 0.038 0.025 1.501 0.133 1.000 1.000
V_sexual 0.040 0.039 1.034 0.301 1.000 1.000lavaan WARNING: starting values imply a correlation larger than 1;
variables involved are: V_fisica C_amenaz 2.4.2 Estimador WLSMV
fit04_b <- cfa(model = model04,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -3.944463e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.lavaan 0.6-6 ended normally after 48 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 84
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 164.755 205.232
Degrees of freedom 142 142
P-value (Chi-square) 0.093 0.000
Scaling correction factor 1.192
Shift parameter 67.055
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6323.983 2674.620
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 2.458
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.996 0.975
Tucker-Lewis Index (TLI) 0.996 0.970
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.022 0.037
90 Percent confidence interval - lower 0.000 0.025
90 Percent confidence interval - upper 0.036 0.048
P-value RMSEA <= 0.05 1.000 0.978
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.094 0.094
Weighted Root Mean Square Residual:
WRMR 0.854 0.854
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.736 0.736
Cadri9a 0.868 0.071 12.179 0.000 0.639 0.639
Cadri12a 1.037 0.066 15.669 0.000 0.763 0.763
Cadri17a 1.139 0.070 16.343 0.000 0.838 0.838
Cadri21a 0.916 0.085 10.783 0.000 0.674 0.674
Cadri24a 0.917 0.061 14.936 0.000 0.674 0.674
Cadri28a 0.949 0.068 13.987 0.000 0.698 0.698
Cadri32a 0.997 0.063 15.790 0.000 0.734 0.734
V_fisica =~
Cadri8a 1.000 0.840 0.840
Cadri25a 0.937 0.075 12.504 0.000 0.787 0.787
Cadri30a 1.049 0.087 12.100 0.000 0.881 0.881
Cadri34a 0.978 0.076 12.802 0.000 0.821 0.821
C_amenaz =~
Cadri29a 1.000 0.569 0.569
Cadri31a 1.240 0.205 6.040 0.000 0.706 0.706
Cadri33a 1.593 0.224 7.118 0.000 0.907 0.907
V_relaci =~
Cadri20a 1.000 0.846 0.846
Cadri35a 0.853 0.108 7.926 0.000 0.721 0.721
V_sexual =~
Cadri2a 1.000 0.602 0.602
Cadri19a 1.303 0.286 4.559 0.000 0.785 0.785
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.464 0.049 9.515 0.000 0.752 0.752
C_amenaz 0.373 0.049 7.662 0.000 0.892 0.892
V_relaci 0.509 0.069 7.434 0.000 0.819 0.819
V_sexual 0.203 0.053 3.842 0.000 0.459 0.459
V_fisica ~~
C_amenaz 0.469 0.061 7.725 0.000 0.981 0.981
V_relaci 0.271 0.108 2.502 0.012 0.381 0.381
V_sexual 0.273 0.062 4.383 0.000 0.541 0.541
C_amenaz ~~
V_relaci 0.317 0.059 5.357 0.000 0.659 0.659
V_sexual 0.257 0.065 3.962 0.000 0.752 0.752
V_relaci ~~
V_sexual 0.285 0.070 4.069 0.000 0.559 0.559
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fisica 0.000 0.000 0.000
C_amenaz 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.459 0.459 0.459
.Cadri9a 0.592 0.592 0.592
.Cadri12a 0.418 0.418 0.418
.Cadri17a 0.298 0.298 0.298
.Cadri21a 0.546 0.546 0.546
.Cadri24a 0.545 0.545 0.545
.Cadri28a 0.513 0.513 0.513
.Cadri32a 0.462 0.462 0.462
.Cadri8a 0.295 0.295 0.295
.Cadri25a 0.381 0.381 0.381
.Cadri30a 0.225 0.225 0.225
.Cadri34a 0.326 0.326 0.326
.Cadri29a 0.676 0.676 0.676
.Cadri31a 0.502 0.502 0.502
.Cadri33a 0.178 0.178 0.178
.Cadri20a 0.285 0.285 0.285
.Cadri35a 0.480 0.480 0.480
.Cadri2a 0.638 0.638 0.638
.Cadri19a 0.384 0.384 0.384
V_verbal 0.541 0.052 10.483 0.000 1.000 1.000
V_fisica 0.705 0.074 9.486 0.000 1.000 1.000
C_amenaz 0.324 0.071 4.582 0.000 1.000 1.000
V_relaci 0.715 0.147 4.883 0.000 1.000 1.000
V_sexual 0.362 0.106 3.413 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.4.3 Gráfico
semPlot::semPaths(fit04_b, whatLabels = "std", label.cex= 1.3,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 2, 15), residScale = 10,
manifests = rev(fit04_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 04")
2.5 Modelo 04 Fork
Se mantienen los cambios de los modelos anteriores - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante) - No considerar al factor Resolución de conflictos como parte del modelo factorial - Sin item 04 y 23 (V. Verbal) - Sin item 03 (V. relacional)
Se incorpora los cambios que se encontrarán en el modelo 07 posteriormente: - Teórica y empíricamente el ítem 21 afecta directamente a las relaciones de la persona - Quitar el ítem 29. Presenta carga compuesta
model04_fork <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri21a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.5.1 Estimador MLR
fit04_a_fork <- cfa(model = model04_fork,
data = cadri_data,
estimator = "MLR")
summary(fit04_a_fork, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-6 ended normally after 140 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 46
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 329.398 201.886
Degrees of freedom 125 125
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.632
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1844.236 940.536
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 1.961
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.879 0.902
Tucker-Lewis Index (TLI) 0.852 0.881
Robust Comparative Fit Index (CFI) 0.919
Robust Tucker-Lewis Index (TLI) 0.901
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3303.012 -3303.012
Scaling correction factor 6.547
for the MLR correction
Loglikelihood unrestricted model (H1) -3138.313 -3138.313
Scaling correction factor 2.954
for the MLR correction
Akaike (AIC) 6698.024 6698.024
Bayesian (BIC) 6872.222 6872.222
Sample-size adjusted Bayesian (BIC) 6726.312 6726.312
Root Mean Square Error of Approximation:
RMSEA 0.071 0.043
90 Percent confidence interval - lower 0.062 0.035
90 Percent confidence interval - upper 0.080 0.052
P-value RMSEA <= 0.05 0.000 0.896
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.041
90 Percent confidence interval - upper 0.069
Standardized Root Mean Square Residual:
SRMR 0.056 0.056
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.541 0.701
Cadri9a 0.755 0.102 7.386 0.000 0.409 0.531
Cadri12a 0.901 0.099 9.058 0.000 0.487 0.692
Cadri17a 0.753 0.085 8.827 0.000 0.407 0.713
Cadri24a 0.630 0.098 6.395 0.000 0.341 0.592
Cadri28a 0.913 0.102 8.993 0.000 0.494 0.625
Cadri32a 0.873 0.091 9.559 0.000 0.472 0.681
V_fisica =~
Cadri8a 1.000 0.261 0.701
Cadri25a 0.740 0.233 3.170 0.002 0.193 0.541
Cadri30a 1.210 0.285 4.249 0.000 0.316 0.741
Cadri34a 0.873 0.223 3.912 0.000 0.228 0.626
C_amenaz =~
Cadri31a 1.000 0.091 0.411
Cadri33a 2.118 1.302 1.626 0.104 0.192 0.716
V_relaci =~
Cadri20a 1.000 0.202 0.595
Cadri21a 1.671 0.401 4.162 0.000 0.338 0.695
Cadri35a 1.110 0.389 2.855 0.004 0.224 0.573
V_sexual =~
Cadri2a 1.000 0.179 0.398
Cadri19a 2.786 1.554 1.793 0.073 0.500 0.645
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.089 0.033 2.737 0.006 0.631 0.631
C_amenaz 0.025 0.018 1.436 0.151 0.517 0.517
V_relaci 0.072 0.033 2.145 0.032 0.656 0.656
V_sexual 0.040 0.021 1.902 0.057 0.408 0.408
V_fisica ~~
C_amenaz 0.021 0.013 1.580 0.114 0.900 0.900
V_relaci 0.013 0.008 1.642 0.101 0.246 0.246
V_sexual 0.022 0.011 2.067 0.039 0.478 0.478
C_amenaz ~~
V_relaci 0.007 0.006 1.096 0.273 0.385 0.385
V_sexual 0.005 0.005 1.002 0.316 0.310 0.310
V_relaci ~~
V_sexual 0.010 0.008 1.269 0.204 0.284 0.284
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.303 0.037 8.086 0.000 0.303 0.509
.Cadri9a 0.424 0.049 8.611 0.000 0.424 0.718
.Cadri12a 0.258 0.041 6.301 0.000 0.258 0.521
.Cadri17a 0.160 0.024 6.614 0.000 0.160 0.491
.Cadri24a 0.215 0.024 8.888 0.000 0.215 0.649
.Cadri28a 0.380 0.044 8.606 0.000 0.380 0.609
.Cadri32a 0.258 0.038 6.854 0.000 0.258 0.536
.Cadri8a 0.071 0.015 4.724 0.000 0.071 0.508
.Cadri25a 0.090 0.020 4.433 0.000 0.090 0.707
.Cadri30a 0.082 0.028 2.932 0.003 0.082 0.451
.Cadri34a 0.081 0.023 3.580 0.000 0.081 0.608
.Cadri31a 0.040 0.025 1.593 0.111 0.040 0.831
.Cadri33a 0.035 0.024 1.450 0.147 0.035 0.488
.Cadri20a 0.075 0.028 2.621 0.009 0.075 0.646
.Cadri21a 0.122 0.034 3.583 0.000 0.122 0.517
.Cadri35a 0.103 0.030 3.435 0.001 0.103 0.672
.Cadri2a 0.171 0.044 3.916 0.000 0.171 0.842
.Cadri19a 0.351 0.161 2.184 0.029 0.351 0.584
V_verbal 0.293 0.059 4.925 0.000 1.000 1.000
V_fisica 0.068 0.032 2.108 0.035 1.000 1.000
C_amenaz 0.008 0.009 0.875 0.382 1.000 1.000
V_relaci 0.041 0.027 1.537 0.124 1.000 1.000
V_sexual 0.032 0.021 1.556 0.120 1.000 1.0002.5.2 Estimador WLSMV
fit04_b_fork <- cfa(model = model04_fork,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -4.288700e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 37 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 81
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 107.159 153.954
Degrees of freedom 125 125
P-value (Chi-square) 0.874 0.040
Scaling correction factor 1.100
Shift parameter 56.530
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6023.208 2620.833
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 2.379
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 0.988
Tucker-Lewis Index (TLI) 1.004 0.986
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.000 0.027
90 Percent confidence interval - lower 0.000 0.006
90 Percent confidence interval - upper 0.015 0.040
P-value RMSEA <= 0.05 1.000 0.999
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.080 0.080
Weighted Root Mean Square Residual:
WRMR 0.721 0.721
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.745 0.745
Cadri9a 0.849 0.071 11.966 0.000 0.632 0.632
Cadri12a 1.041 0.065 16.061 0.000 0.775 0.775
Cadri17a 1.128 0.069 16.390 0.000 0.840 0.840
Cadri24a 0.917 0.061 15.021 0.000 0.683 0.683
Cadri28a 0.936 0.067 13.908 0.000 0.697 0.697
Cadri32a 0.994 0.062 16.065 0.000 0.740 0.740
V_fisica =~
Cadri8a 1.000 0.839 0.839
Cadri25a 0.934 0.073 12.798 0.000 0.784 0.784
Cadri30a 1.051 0.084 12.543 0.000 0.882 0.882
Cadri34a 0.981 0.075 13.016 0.000 0.823 0.823
C_amenaz =~
Cadri31a 1.000 0.780 0.780
Cadri33a 1.229 0.156 7.880 0.000 0.959 0.959
V_relaci =~
Cadri20a 1.000 0.845 0.845
Cadri21a 1.004 0.131 7.660 0.000 0.849 0.849
Cadri35a 0.849 0.106 7.986 0.000 0.718 0.718
V_sexual =~
Cadri2a 1.000 0.596 0.596
Cadri19a 1.329 0.288 4.620 0.000 0.792 0.792
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fisica 0.471 0.049 9.633 0.000 0.754 0.754
C_amenaz 0.449 0.066 6.834 0.000 0.772 0.772
V_relaci 0.459 0.068 6.719 0.000 0.730 0.730
V_sexual 0.202 0.053 3.826 0.000 0.455 0.455
V_fisica ~~
C_amenaz 0.617 0.080 7.756 0.000 0.942 0.942
V_relaci 0.316 0.088 3.612 0.000 0.446 0.446
V_sexual 0.269 0.061 4.421 0.000 0.539 0.539
C_amenaz ~~
V_relaci 0.359 0.064 5.640 0.000 0.545 0.545
V_sexual 0.318 0.061 5.212 0.000 0.683 0.683
V_relaci ~~
V_sexual 0.232 0.056 4.111 0.000 0.461 0.461
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fisica 0.000 0.000 0.000
C_amenaz 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.445 0.445 0.445
.Cadri9a 0.600 0.600 0.600
.Cadri12a 0.399 0.399 0.399
.Cadri17a 0.294 0.294 0.294
.Cadri24a 0.534 0.534 0.534
.Cadri28a 0.514 0.514 0.514
.Cadri32a 0.452 0.452 0.452
.Cadri8a 0.296 0.296 0.296
.Cadri25a 0.386 0.386 0.386
.Cadri30a 0.222 0.222 0.222
.Cadri34a 0.322 0.322 0.322
.Cadri31a 0.391 0.391 0.391
.Cadri33a 0.080 0.080 0.080
.Cadri20a 0.286 0.286 0.286
.Cadri21a 0.280 0.280 0.280
.Cadri35a 0.485 0.485 0.485
.Cadri2a 0.645 0.645 0.645
.Cadri19a 0.372 0.372 0.372
V_verbal 0.555 0.052 10.596 0.000 1.000 1.000
V_fisica 0.704 0.072 9.714 0.000 1.000 1.000
C_amenaz 0.609 0.114 5.341 0.000 1.000 1.000
V_relaci 0.714 0.136 5.268 0.000 1.000 1.000
V_sexual 0.355 0.104 3.421 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.5.3 Fiabilidad del modelo categórico
For constructs with categorical indicators, the alpha and the average variance extracted are calculated from polychoric (polyserial) correlations, not from Pearson correlations. V_verbal V_fisica C_amenaz V_relaci V_sexual
alpha 0.8869414 0.9004611 0.8562722 0.8443092 0.6416203
omega 0.8206988 0.7758095 0.6651273 0.6705323 0.5201420
omega2 0.8206988 0.7758095 0.6651273 0.6705323 0.5201420
omega3 0.8260569 0.7763087 0.6651274 0.6695223 0.5201421
avevar 0.5373876 0.6934556 0.7646412 0.6498375 0.49162502.5.4 Gráfico
semPlot::semPaths(fit04_b_fork, whatLabels = "std", label.cex= 1.3,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 2, 15), residScale = 10,
manifests = rev(fit04_b_fork@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 04 con ajustes del modelo 07")
2.6 Modelo 05
Se mantienen los cambios de los modelos 01 al 04 - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante) - No considerar al factor Resolución de conflictos como parte del modelo factorial - Sin item 04 y 23 (V. Verbal) - Sin item 03 (V. relacional)
Se adiciona el siguiente cambio: - Unir factor de conducta amenzante con violencia física debido a que el contenido de los ítems hacen alusión a la intención de hacer un daño físico. Esto es mejor a generar errores correlacionados entre ítems que pertenecen a diferentes dimensiones.
model05 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a +
Cadri24a + Cadri28a + Cadri32a
V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.6.1 Estimador MLR
fit05_a <- cfa(model = model05,
data = cadri_data,
estimator = "MLR")
summary(fit05_a, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-6 ended normally after 98 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 44
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 463.603 269.564
Degrees of freedom 146 146
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.720
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1937.884 987.012
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 1.963
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.820 0.849
Tucker-Lewis Index (TLI) 0.789 0.823
Robust Comparative Fit Index (CFI) 0.867
Robust Tucker-Lewis Index (TLI) 0.845
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3474.738 -3474.738
Scaling correction factor 6.736
for the MLR correction
Loglikelihood unrestricted model (H1) -3242.936 -3242.936
Scaling correction factor 2.881
for the MLR correction
Akaike (AIC) 7037.476 7037.476
Bayesian (BIC) 7204.099 7204.099
Sample-size adjusted Bayesian (BIC) 7064.534 7064.534
Root Mean Square Error of Approximation:
RMSEA 0.082 0.051
90 Percent confidence interval - lower 0.073 0.044
90 Percent confidence interval - upper 0.090 0.058
P-value RMSEA <= 0.05 0.000 0.405
Robust RMSEA 0.067
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.079
Standardized Root Mean Square Residual:
SRMR 0.072 0.072
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.540 0.699
Cadri9a 0.755 0.103 7.315 0.000 0.407 0.530
Cadri12a 0.888 0.098 9.050 0.000 0.479 0.680
Cadri17a 0.756 0.084 9.028 0.000 0.408 0.715
Cadri21a 0.441 0.110 4.024 0.000 0.238 0.489
Cadri24a 0.636 0.099 6.449 0.000 0.343 0.597
Cadri28a 0.910 0.098 9.252 0.000 0.491 0.622
Cadri32a 0.869 0.091 9.543 0.000 0.469 0.676
V_fis_am =~
Cadri8a 1.000 0.258 0.693
Cadri25a 0.766 0.231 3.315 0.001 0.198 0.554
Cadri30a 1.226 0.289 4.235 0.000 0.317 0.742
Cadri34a 0.887 0.219 4.059 0.000 0.229 0.629
Cadri29a 0.374 0.224 1.664 0.096 0.096 0.251
Cadri31a 0.336 0.214 1.572 0.116 0.087 0.394
Cadri33a 0.640 0.170 3.758 0.000 0.165 0.617
V_relaci =~
Cadri20a 1.000 0.193 0.569
Cadri35a 1.122 0.436 2.574 0.010 0.217 0.554
V_sexual =~
Cadri2a 1.000 0.199 0.442
Cadri19a 2.257 2.263 0.997 0.319 0.450 0.581
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.088 0.031 2.797 0.005 0.629 0.629
V_relaci 0.076 0.036 2.121 0.034 0.728 0.728
V_sexual 0.045 0.030 1.501 0.133 0.421 0.421
V_fis_am ~~
V_relaci 0.014 0.011 1.351 0.177 0.286 0.286
V_sexual 0.025 0.014 1.795 0.073 0.486 0.486
V_relaci ~~
V_sexual 0.016 0.022 0.728 0.467 0.417 0.417
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.305 0.036 8.481 0.000 0.305 0.511
.Cadri9a 0.425 0.049 8.604 0.000 0.425 0.719
.Cadri12a 0.266 0.041 6.415 0.000 0.266 0.537
.Cadri17a 0.160 0.023 6.811 0.000 0.160 0.489
.Cadri21a 0.180 0.034 5.258 0.000 0.180 0.761
.Cadri24a 0.213 0.024 8.875 0.000 0.213 0.644
.Cadri28a 0.382 0.044 8.705 0.000 0.382 0.613
.Cadri32a 0.261 0.039 6.789 0.000 0.261 0.543
.Cadri8a 0.072 0.015 4.770 0.000 0.072 0.519
.Cadri25a 0.088 0.020 4.440 0.000 0.088 0.693
.Cadri30a 0.082 0.028 2.888 0.004 0.082 0.449
.Cadri34a 0.080 0.022 3.579 0.000 0.080 0.604
.Cadri29a 0.139 0.027 5.149 0.000 0.139 0.937
.Cadri31a 0.041 0.023 1.760 0.078 0.041 0.845
.Cadri33a 0.044 0.017 2.690 0.007 0.044 0.619
.Cadri20a 0.078 0.030 2.642 0.008 0.078 0.676
.Cadri35a 0.106 0.029 3.656 0.000 0.106 0.693
.Cadri2a 0.164 0.046 3.543 0.000 0.164 0.805
.Cadri19a 0.398 0.213 1.870 0.061 0.398 0.663
V_verbal 0.291 0.059 4.959 0.000 1.000 1.000
V_fis_am 0.067 0.032 2.091 0.037 1.000 1.000
V_relaci 0.037 0.025 1.485 0.137 1.000 1.000
V_sexual 0.040 0.041 0.969 0.332 1.000 1.0002.6.2 Estimador WLSMV
fit05_b <- cfa(model = model05,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -5.576008e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 34 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 80
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 180.772 216.053
Degrees of freedom 146 146
P-value (Chi-square) 0.027 0.000
Scaling correction factor 1.241
Shift parameter 70.420
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6323.983 2674.620
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 2.458
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.994 0.972
Tucker-Lewis Index (TLI) 0.993 0.967
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.027 0.038
90 Percent confidence interval - lower 0.010 0.027
90 Percent confidence interval - upper 0.039 0.049
P-value RMSEA <= 0.05 1.000 0.966
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.099 0.099
Weighted Root Mean Square Residual:
WRMR 0.894 0.894
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.736 0.736
Cadri9a 0.866 0.071 12.118 0.000 0.637 0.637
Cadri12a 1.040 0.066 15.719 0.000 0.765 0.765
Cadri17a 1.138 0.070 16.357 0.000 0.837 0.837
Cadri21a 0.916 0.085 10.791 0.000 0.674 0.674
Cadri24a 0.917 0.062 14.884 0.000 0.675 0.675
Cadri28a 0.946 0.068 13.968 0.000 0.696 0.696
Cadri32a 0.999 0.063 15.823 0.000 0.735 0.735
V_fis_am =~
Cadri8a 1.000 0.820 0.820
Cadri25a 0.939 0.075 12.442 0.000 0.770 0.770
Cadri30a 1.042 0.085 12.245 0.000 0.854 0.854
Cadri34a 0.977 0.076 12.772 0.000 0.801 0.801
Cadri29a 0.714 0.090 7.953 0.000 0.586 0.586
Cadri31a 0.919 0.109 8.418 0.000 0.753 0.753
Cadri33a 1.142 0.088 13.022 0.000 0.937 0.937
V_relaci =~
Cadri20a 1.000 0.837 0.837
Cadri35a 0.872 0.112 7.807 0.000 0.729 0.729
V_sexual =~
Cadri2a 1.000 0.597 0.597
Cadri19a 1.324 0.294 4.499 0.000 0.791 0.791
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.480 0.046 10.350 0.000 0.795 0.795
V_relaci 0.505 0.069 7.352 0.000 0.820 0.820
V_sexual 0.201 0.053 3.803 0.000 0.457 0.457
V_fis_am ~~
V_relaci 0.360 0.074 4.853 0.000 0.525 0.525
V_sexual 0.305 0.062 4.933 0.000 0.623 0.623
V_relaci ~~
V_sexual 0.277 0.069 4.037 0.000 0.555 0.555
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fis_am 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.459 0.459 0.459
.Cadri9a 0.594 0.594 0.594
.Cadri12a 0.414 0.414 0.414
.Cadri17a 0.299 0.299 0.299
.Cadri21a 0.546 0.546 0.546
.Cadri24a 0.545 0.545 0.545
.Cadri28a 0.516 0.516 0.516
.Cadri32a 0.460 0.460 0.460
.Cadri8a 0.328 0.328 0.328
.Cadri25a 0.407 0.407 0.407
.Cadri30a 0.271 0.271 0.271
.Cadri34a 0.358 0.358 0.358
.Cadri29a 0.657 0.657 0.657
.Cadri31a 0.432 0.432 0.432
.Cadri33a 0.123 0.123 0.123
.Cadri20a 0.300 0.300 0.300
.Cadri35a 0.468 0.468 0.468
.Cadri2a 0.643 0.643 0.643
.Cadri19a 0.375 0.375 0.375
V_verbal 0.541 0.052 10.475 0.000 1.000 1.000
V_fis_am 0.672 0.075 8.973 0.000 1.000 1.000
V_relaci 0.700 0.145 4.826 0.000 1.000 1.000
V_sexual 0.357 0.106 3.369 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.6.3 Gráfico
semPlot::semPaths(fit05_b, whatLabels = "std", label.cex= 1.3,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 2, 15), residScale = 10,
manifests = rev(fit05_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 05. Conducta amenzante y física juntos")
2.7 Modelo 06
Se mantienen los cambios de los modelos 01 al 04 y 05 - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante) - No considerar al factor Resolución de conflictos como parte del modelo factorial - Sin item 04 y 23 (V. Verbal) - Sin item 03 (V. relacional) - Unir factor de conducta amenzante con violencia física
Se adiciona el siguiente cambio: - Ítem 21 pasa a violencia relacional: Su contenido hace referencia directamente a las relaciones de las personas
Nota: Alerta con el ítem 29. Puede tener sesgo por deseabilidad social (el modelo 07 será sin este ítem)
model06 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri29a + Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri21a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.7.1 Estimador MLR
fit06_a <- cfa(model = model06,
data = cadri_data,
estimator = "MLR")
summary(fit06_a, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-6 ended normally after 100 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 44
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 418.887 240.974
Degrees of freedom 146 146
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.738
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1937.884 987.012
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 1.963
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.846 0.884
Tucker-Lewis Index (TLI) 0.819 0.864
Robust Comparative Fit Index (CFI) 0.897
Robust Tucker-Lewis Index (TLI) 0.879
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3452.379 -3452.379
Scaling correction factor 6.675
for the MLR correction
Loglikelihood unrestricted model (H1) -3242.936 -3242.936
Scaling correction factor 2.881
for the MLR correction
Akaike (AIC) 6992.759 6992.759
Bayesian (BIC) 7159.382 7159.382
Sample-size adjusted Bayesian (BIC) 7019.817 7019.817
Root Mean Square Error of Approximation:
RMSEA 0.076 0.045
90 Percent confidence interval - lower 0.067 0.037
90 Percent confidence interval - upper 0.084 0.052
P-value RMSEA <= 0.05 0.000 0.875
Robust RMSEA 0.059
90 Percent confidence interval - lower 0.045
90 Percent confidence interval - upper 0.072
Standardized Root Mean Square Residual:
SRMR 0.068 0.068
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.541 0.701
Cadri9a 0.757 0.103 7.356 0.000 0.410 0.533
Cadri12a 0.898 0.101 8.923 0.000 0.486 0.690
Cadri17a 0.754 0.085 8.842 0.000 0.408 0.714
Cadri24a 0.630 0.098 6.423 0.000 0.341 0.593
Cadri28a 0.913 0.102 8.942 0.000 0.494 0.625
Cadri32a 0.872 0.092 9.464 0.000 0.472 0.680
V_fis_am =~
Cadri8a 1.000 0.258 0.693
Cadri25a 0.765 0.230 3.331 0.001 0.198 0.553
Cadri30a 1.228 0.289 4.247 0.000 0.317 0.744
Cadri34a 0.882 0.216 4.092 0.000 0.228 0.626
Cadri29a 0.374 0.225 1.663 0.096 0.097 0.251
Cadri31a 0.339 0.213 1.588 0.112 0.088 0.398
Cadri33a 0.641 0.168 3.805 0.000 0.166 0.618
V_relaci =~
Cadri20a 1.000 0.197 0.581
Cadri21a 1.752 0.417 4.203 0.000 0.346 0.711
Cadri35a 1.133 0.397 2.852 0.004 0.224 0.571
V_sexual =~
Cadri2a 1.000 0.175 0.389
Cadri19a 2.914 1.811 1.609 0.108 0.511 0.660
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.089 0.031 2.838 0.005 0.636 0.636
V_relaci 0.070 0.033 2.112 0.035 0.653 0.653
V_sexual 0.038 0.023 1.681 0.093 0.403 0.403
V_fis_am ~~
V_relaci 0.015 0.008 1.878 0.060 0.302 0.302
V_sexual 0.021 0.011 1.878 0.060 0.467 0.467
V_relaci ~~
V_sexual 0.009 0.008 1.165 0.244 0.271 0.271
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.303 0.037 8.092 0.000 0.303 0.509
.Cadri9a 0.423 0.049 8.600 0.000 0.423 0.716
.Cadri12a 0.259 0.041 6.307 0.000 0.259 0.523
.Cadri17a 0.160 0.024 6.719 0.000 0.160 0.490
.Cadri24a 0.215 0.024 8.892 0.000 0.215 0.649
.Cadri28a 0.380 0.044 8.608 0.000 0.380 0.609
.Cadri32a 0.259 0.038 6.806 0.000 0.259 0.538
.Cadri8a 0.072 0.015 4.777 0.000 0.072 0.519
.Cadri25a 0.089 0.020 4.457 0.000 0.089 0.694
.Cadri30a 0.081 0.028 2.894 0.004 0.081 0.447
.Cadri34a 0.081 0.022 3.612 0.000 0.081 0.608
.Cadri29a 0.139 0.027 5.157 0.000 0.139 0.937
.Cadri31a 0.041 0.023 1.760 0.078 0.041 0.842
.Cadri33a 0.044 0.017 2.681 0.007 0.044 0.618
.Cadri20a 0.077 0.029 2.649 0.008 0.077 0.663
.Cadri21a 0.117 0.033 3.520 0.000 0.117 0.494
.Cadri35a 0.103 0.030 3.423 0.001 0.103 0.674
.Cadri2a 0.173 0.044 3.968 0.000 0.173 0.849
.Cadri19a 0.339 0.179 1.901 0.057 0.339 0.565
V_verbal 0.293 0.060 4.892 0.000 1.000 1.000
V_fis_am 0.067 0.032 2.095 0.036 1.000 1.000
V_relaci 0.039 0.026 1.502 0.133 1.000 1.000
V_sexual 0.031 0.022 1.416 0.157 1.000 1.0002.7.2 Estimador WLSMV
fit06_b <- cfa(model = model06,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -4.365953e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 35 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 80
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 158.470 199.551
Degrees of freedom 146 146
P-value (Chi-square) 0.227 0.002
Scaling correction factor 1.226
Shift parameter 70.273
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6323.983 2674.620
Degrees of freedom 171 171
P-value 0.000 0.000
Scaling correction factor 2.458
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998 0.979
Tucker-Lewis Index (TLI) 0.998 0.975
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.016 0.034
90 Percent confidence interval - lower 0.000 0.021
90 Percent confidence interval - upper 0.032 0.045
P-value RMSEA <= 0.05 1.000 0.994
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.096 0.096
Weighted Root Mean Square Residual:
WRMR 0.837 0.837
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.741 0.741
Cadri9a 0.863 0.072 12.064 0.000 0.639 0.639
Cadri12a 1.040 0.066 15.786 0.000 0.770 0.770
Cadri17a 1.139 0.070 16.289 0.000 0.843 0.843
Cadri24a 0.919 0.062 14.847 0.000 0.681 0.681
Cadri28a 0.947 0.068 13.948 0.000 0.701 0.701
Cadri32a 0.999 0.063 15.761 0.000 0.740 0.740
V_fis_am =~
Cadri8a 1.000 0.820 0.820
Cadri25a 0.940 0.075 12.449 0.000 0.770 0.770
Cadri30a 1.042 0.085 12.263 0.000 0.854 0.854
Cadri34a 0.978 0.077 12.756 0.000 0.802 0.802
Cadri29a 0.716 0.090 7.974 0.000 0.587 0.587
Cadri31a 0.916 0.108 8.469 0.000 0.751 0.751
Cadri33a 1.143 0.088 12.942 0.000 0.937 0.937
V_relaci =~
Cadri20a 1.000 0.840 0.840
Cadri21a 1.002 0.132 7.607 0.000 0.842 0.842
Cadri35a 0.870 0.107 8.099 0.000 0.731 0.731
V_sexual =~
Cadri2a 1.000 0.597 0.597
Cadri19a 1.326 0.295 4.490 0.000 0.791 0.791
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.483 0.047 10.355 0.000 0.795 0.795
V_relaci 0.455 0.068 6.690 0.000 0.731 0.731
V_sexual 0.201 0.054 3.765 0.000 0.456 0.456
V_fis_am ~~
V_relaci 0.374 0.070 5.376 0.000 0.544 0.544
V_sexual 0.305 0.062 4.923 0.000 0.623 0.623
V_relaci ~~
V_sexual 0.231 0.057 4.045 0.000 0.462 0.462
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri29a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fis_am 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri29a|t1 1.292 0.095 13.534 0.000 1.292 1.292
Cadri29a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.451 0.451 0.451
.Cadri9a 0.592 0.592 0.592
.Cadri12a 0.407 0.407 0.407
.Cadri17a 0.289 0.289 0.289
.Cadri24a 0.537 0.537 0.537
.Cadri28a 0.508 0.508 0.508
.Cadri32a 0.452 0.452 0.452
.Cadri8a 0.328 0.328 0.328
.Cadri25a 0.407 0.407 0.407
.Cadri30a 0.270 0.270 0.270
.Cadri34a 0.358 0.358 0.358
.Cadri29a 0.656 0.656 0.656
.Cadri31a 0.436 0.436 0.436
.Cadri33a 0.122 0.122 0.122
.Cadri20a 0.294 0.294 0.294
.Cadri21a 0.291 0.291 0.291
.Cadri35a 0.466 0.466 0.466
.Cadri2a 0.644 0.644 0.644
.Cadri19a 0.374 0.374 0.374
V_verbal 0.549 0.053 10.436 0.000 1.000 1.000
V_fis_am 0.672 0.075 8.968 0.000 1.000 1.000
V_relaci 0.706 0.135 5.217 0.000 1.000 1.000
V_sexual 0.356 0.106 3.361 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri29a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.7.3 Gráfico
semPlot::semPaths(fit06_b, whatLabels = "std", label.cex= 1.3,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 1, 15), residScale = 10,
manifests = rev(fit06_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 06 basado en modelo 05")
2.8 Modelo 07
Se mantienen los cambios de los modelos 01 al 04 y 05 - Sin item 15, 13 (V Sexual) - Sin item 05 (Conducta amenzante) - No considerar al factor Resolución de conflictos como parte del modelo factorial - Sin item 04 y 23 (V. Verbal) - Sin item 03 (V. relacional) - Unir factor de conducta amenzante con violencia física - Ítem 21 pasa a violencia relacional
Se adiciona el siguiente cambio: - Quitar el ítem 29. Presenta carga compuesta
model07 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri21a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a"2.8.1 Estimador MLR
fit07_a <- cfa(model = model07,
data = cadri_data,
estimator = "MLR")
summary(fit07_a, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-6 ended normally after 105 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 42
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 342.193 198.818
Degrees of freedom 129 129
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.721
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1844.236 940.536
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 1.961
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.874 0.911
Tucker-Lewis Index (TLI) 0.850 0.895
Robust Comparative Fit Index (CFI) 0.922
Robust Tucker-Lewis Index (TLI) 0.908
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3309.409 -3309.409
Scaling correction factor 6.740
for the MLR correction
Loglikelihood unrestricted model (H1) -3138.313 -3138.313
Scaling correction factor 2.954
for the MLR correction
Akaike (AIC) 6702.819 6702.819
Bayesian (BIC) 6861.868 6861.868
Sample-size adjusted Bayesian (BIC) 6728.647 6728.647
Root Mean Square Error of Approximation:
RMSEA 0.071 0.041
90 Percent confidence interval - lower 0.062 0.032
90 Percent confidence interval - upper 0.080 0.049
P-value RMSEA <= 0.05 0.000 0.967
Robust RMSEA 0.053
90 Percent confidence interval - lower 0.038
90 Percent confidence interval - upper 0.068
Standardized Root Mean Square Residual:
SRMR 0.057 0.057
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.542 0.702
Cadri9a 0.755 0.102 7.380 0.000 0.409 0.532
Cadri12a 0.898 0.100 8.997 0.000 0.486 0.691
Cadri17a 0.752 0.085 8.844 0.000 0.408 0.714
Cadri24a 0.629 0.098 6.432 0.000 0.341 0.593
Cadri28a 0.911 0.102 8.972 0.000 0.493 0.625
Cadri32a 0.872 0.091 9.538 0.000 0.473 0.681
V_fis_am =~
Cadri8a 1.000 0.260 0.697
Cadri25a 0.752 0.230 3.275 0.001 0.195 0.547
Cadri30a 1.230 0.283 4.350 0.000 0.319 0.748
Cadri34a 0.880 0.222 3.959 0.000 0.228 0.627
Cadri31a 0.334 0.206 1.623 0.105 0.087 0.393
Cadri33a 0.651 0.163 3.983 0.000 0.169 0.631
V_relaci =~
Cadri20a 1.000 0.197 0.581
Cadri21a 1.751 0.414 4.231 0.000 0.346 0.711
Cadri35a 1.134 0.398 2.851 0.004 0.224 0.571
V_sexual =~
Cadri2a 1.000 0.175 0.387
Cadri19a 2.943 1.828 1.610 0.108 0.514 0.663
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.087 0.031 2.766 0.006 0.618 0.618
V_relaci 0.070 0.033 2.118 0.034 0.653 0.653
V_sexual 0.038 0.023 1.682 0.093 0.401 0.401
V_fis_am ~~
V_relaci 0.015 0.008 1.728 0.084 0.283 0.283
V_sexual 0.020 0.011 1.921 0.055 0.449 0.449
V_relaci ~~
V_sexual 0.009 0.008 1.160 0.246 0.270 0.270
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.303 0.037 8.098 0.000 0.303 0.508
.Cadri9a 0.424 0.049 8.606 0.000 0.424 0.717
.Cadri12a 0.259 0.041 6.312 0.000 0.259 0.523
.Cadri17a 0.160 0.024 6.669 0.000 0.160 0.491
.Cadri24a 0.215 0.024 8.875 0.000 0.215 0.649
.Cadri28a 0.380 0.044 8.610 0.000 0.380 0.610
.Cadri32a 0.258 0.038 6.830 0.000 0.258 0.536
.Cadri8a 0.071 0.015 4.888 0.000 0.071 0.515
.Cadri25a 0.089 0.020 4.494 0.000 0.089 0.701
.Cadri30a 0.080 0.028 2.826 0.005 0.080 0.440
.Cadri34a 0.080 0.022 3.639 0.000 0.080 0.607
.Cadri31a 0.041 0.024 1.725 0.085 0.041 0.845
.Cadri33a 0.043 0.016 2.711 0.007 0.043 0.602
.Cadri20a 0.077 0.029 2.649 0.008 0.077 0.663
.Cadri21a 0.117 0.033 3.526 0.000 0.117 0.495
.Cadri35a 0.103 0.030 3.426 0.001 0.103 0.674
.Cadri2a 0.173 0.044 3.945 0.000 0.173 0.850
.Cadri19a 0.337 0.181 1.858 0.063 0.337 0.561
V_verbal 0.293 0.060 4.915 0.000 1.000 1.000
V_fis_am 0.067 0.032 2.098 0.036 1.000 1.000
V_relaci 0.039 0.026 1.505 0.132 1.000 1.000
V_sexual 0.030 0.021 1.427 0.154 1.000 1.0002.8.2 Estimador WLSMV
fit07_b <- cfa(model = model07,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -5.186196e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 32 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 77
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 112.321 157.799
Degrees of freedom 129 129
P-value (Chi-square) 0.852 0.043
Scaling correction factor 1.139
Shift parameter 59.196
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6023.208 2620.833
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 2.379
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 0.988
Tucker-Lewis Index (TLI) 1.003 0.986
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.000 0.026
90 Percent confidence interval - lower 0.000 0.005
90 Percent confidence interval - upper 0.016 0.039
P-value RMSEA <= 0.05 1.000 0.999
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.082 0.082
Weighted Root Mean Square Residual:
WRMR 0.738 0.738
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.745 0.745
Cadri9a 0.849 0.071 11.962 0.000 0.632 0.632
Cadri12a 1.040 0.065 16.093 0.000 0.775 0.775
Cadri17a 1.128 0.069 16.414 0.000 0.840 0.840
Cadri24a 0.917 0.061 15.010 0.000 0.683 0.683
Cadri28a 0.936 0.067 13.895 0.000 0.697 0.697
Cadri32a 0.995 0.062 16.074 0.000 0.741 0.741
V_fis_am =~
Cadri8a 1.000 0.829 0.829
Cadri25a 0.935 0.073 12.808 0.000 0.775 0.775
Cadri30a 1.046 0.082 12.726 0.000 0.867 0.867
Cadri34a 0.980 0.075 13.032 0.000 0.813 0.813
Cadri31a 0.952 0.113 8.420 0.000 0.790 0.790
Cadri33a 1.130 0.086 13.147 0.000 0.937 0.937
V_relaci =~
Cadri20a 1.000 0.843 0.843
Cadri21a 1.002 0.131 7.664 0.000 0.845 0.845
Cadri35a 0.860 0.108 7.988 0.000 0.725 0.725
V_sexual =~
Cadri2a 1.000 0.593 0.593
Cadri19a 1.344 0.296 4.545 0.000 0.797 0.797
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal ~~
V_fis_am 0.474 0.046 10.191 0.000 0.767 0.767
V_relaci 0.459 0.068 6.737 0.000 0.731 0.731
V_sexual 0.200 0.053 3.790 0.000 0.454 0.454
V_fis_am ~~
V_relaci 0.343 0.071 4.802 0.000 0.491 0.491
V_sexual 0.296 0.059 5.047 0.000 0.602 0.602
V_relaci ~~
V_sexual 0.230 0.056 4.086 0.000 0.460 0.460
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
V_verbal 0.000 0.000 0.000
V_fis_am 0.000 0.000 0.000
V_relaci 0.000 0.000 0.000
V_sexual 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.445 0.445 0.445
.Cadri9a 0.600 0.600 0.600
.Cadri12a 0.400 0.400 0.400
.Cadri17a 0.294 0.294 0.294
.Cadri24a 0.534 0.534 0.534
.Cadri28a 0.514 0.514 0.514
.Cadri32a 0.451 0.451 0.451
.Cadri8a 0.312 0.312 0.312
.Cadri25a 0.399 0.399 0.399
.Cadri30a 0.248 0.248 0.248
.Cadri34a 0.340 0.340 0.340
.Cadri31a 0.376 0.376 0.376
.Cadri33a 0.122 0.122 0.122
.Cadri20a 0.290 0.290 0.290
.Cadri21a 0.287 0.287 0.287
.Cadri35a 0.475 0.475 0.475
.Cadri2a 0.649 0.649 0.649
.Cadri19a 0.365 0.365 0.365
V_verbal 0.555 0.052 10.596 0.000 1.000 1.000
V_fis_am 0.688 0.074 9.275 0.000 1.000 1.000
V_relaci 0.710 0.135 5.244 0.000 1.000 1.000
V_sexual 0.351 0.104 3.377 0.001 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.8.3 Fiabilidad del modelo WLSMV
For constructs with categorical indicators, the alpha and the average variance extracted are calculated from polychoric (polyserial) correlations, not from Pearson correlations. V_verbal V_fis_am V_relaci V_sexual
alpha 0.8869414 0.9257101 0.8443092 0.6416203
omega 0.8206993 0.8162186 0.6694553 0.5237891
omega2 0.8206993 0.8162186 0.6694553 0.5237891
omega3 0.8260642 0.8205415 0.6686990 0.5237894
avevar 0.5373890 0.7006245 0.6496149 0.49313542.8.4 Gráfico
semPlot::semPaths(fit07_b, whatLabels = "std", label.cex= 1.3,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 8, sizeLat2 = 4,
sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 15, 1, 15), residScale = 10,
manifests = rev(fit07_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 07 basado en modelo 05")
#
# semPlot::semPaths(fit06_b, whatLabels = "std", label.cex= 1.3,
# edge.label.cex = 0.8, nCharEdges = 3,
# nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
# sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
# thresholds = F, groups = "latents", pastel = TRUE,
# exoVar = FALSE, edge.color = "black",
# curvature = 2, mar = c(1, 15, 1, 15), residScale = 10,
# manifests = rev(fit06_b@Data@ordered))
#
# title("Modelo Factorial del CADRI \n Modelo 06")2.9 Modelo 08: Segundo orden
Este modelo de segundo orden está basado en el Modelo 04 Fork que incluye los cambios encontrados en el modelo 07 con respecto a los ítems, pero considerando al factor de conducta amenzante.
model08 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a +
Cadri24a + Cadri28a + Cadri32a
V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
C_amenaz =~ Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri21a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a
# Segundo orden
Violencia =~ V_fisica + V_verbal + C_amenaz + V_relaci + V_sexual"2.9.1 Estimador MLR
fit08_a <- cfa(model = model08,
data = cadri_data,
estimator = "MLR")
summary(fit08_a, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-6 ended normally after 105 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 41
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 403.491 249.682
Degrees of freedom 130 130
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.616
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1844.236 940.536
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 1.961
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.838 0.848
Tucker-Lewis Index (TLI) 0.810 0.821
Robust Comparative Fit Index (CFI) 0.875
Robust Tucker-Lewis Index (TLI) 0.853
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3340.058 -3340.058
Scaling correction factor 7.195
for the MLR correction
Loglikelihood unrestricted model (H1) -3138.313 -3138.313
Scaling correction factor 2.954
for the MLR correction
Akaike (AIC) 6762.117 6762.117
Bayesian (BIC) 6917.379 6917.379
Sample-size adjusted Bayesian (BIC) 6787.330 6787.330
Root Mean Square Error of Approximation:
RMSEA 0.080 0.053
90 Percent confidence interval - lower 0.072 0.045
90 Percent confidence interval - upper 0.089 0.061
P-value RMSEA <= 0.05 0.000 0.248
Robust RMSEA 0.068
90 Percent confidence interval - lower 0.055
90 Percent confidence interval - upper 0.080
Standardized Root Mean Square Residual:
SRMR 0.077 0.077
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.541 0.701
Cadri9a 0.753 0.103 7.295 0.000 0.408 0.530
Cadri12a 0.919 0.105 8.734 0.000 0.498 0.707
Cadri17a 0.744 0.088 8.469 0.000 0.403 0.706
Cadri24a 0.616 0.096 6.399 0.000 0.334 0.580
Cadri28a 0.914 0.109 8.419 0.000 0.495 0.627
Cadri32a 0.874 0.093 9.364 0.000 0.473 0.682
V_fisica =~
Cadri8a 1.000 0.259 0.696
Cadri25a 0.786 0.243 3.236 0.001 0.204 0.571
Cadri30a 1.190 0.255 4.674 0.000 0.308 0.723
Cadri34a 0.892 0.229 3.888 0.000 0.231 0.635
C_amenaz =~
Cadri31a 1.000 0.094 0.425
Cadri33a 1.980 1.165 1.699 0.089 0.185 0.692
V_relaci =~
Cadri20a 1.000 0.193 0.566
Cadri21a 1.858 0.549 3.385 0.001 0.358 0.736
Cadri35a 1.138 0.387 2.936 0.003 0.219 0.559
V_sexual =~
Cadri2a 1.000 0.168 0.372
Cadri19a 3.183 1.889 1.685 0.092 0.534 0.689
Violencia =~
V_fisica 1.000 0.917 0.917
V_verbal 1.621 0.607 2.669 0.008 0.711 0.711
C_amenaz 0.356 0.220 1.621 0.105 0.904 0.904
V_relaci 0.369 0.231 1.600 0.110 0.455 0.455
V_sexual 0.341 0.276 1.237 0.216 0.483 0.483
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.303 0.041 7.412 0.000 0.303 0.508
.Cadri9a 0.425 0.050 8.583 0.000 0.425 0.719
.Cadri12a 0.248 0.041 6.086 0.000 0.248 0.500
.Cadri17a 0.164 0.025 6.484 0.000 0.164 0.502
.Cadri24a 0.219 0.024 9.018 0.000 0.219 0.663
.Cadri28a 0.379 0.045 8.362 0.000 0.379 0.607
.Cadri32a 0.258 0.037 6.943 0.000 0.258 0.535
.Cadri8a 0.072 0.015 4.888 0.000 0.072 0.516
.Cadri25a 0.086 0.021 4.178 0.000 0.086 0.674
.Cadri30a 0.087 0.030 2.893 0.004 0.087 0.477
.Cadri34a 0.079 0.023 3.506 0.000 0.079 0.597
.Cadri31a 0.040 0.025 1.599 0.110 0.040 0.819
.Cadri33a 0.037 0.024 1.562 0.118 0.037 0.521
.Cadri20a 0.078 0.031 2.520 0.012 0.078 0.679
.Cadri21a 0.108 0.039 2.802 0.005 0.108 0.459
.Cadri35a 0.105 0.033 3.167 0.002 0.105 0.687
.Cadri2a 0.175 0.044 3.986 0.000 0.175 0.861
.Cadri19a 0.315 0.184 1.717 0.086 0.315 0.525
.V_verbal 0.145 0.056 2.596 0.009 0.494 0.494
.V_fisica 0.011 0.012 0.911 0.362 0.160 0.160
.C_amenaz 0.002 0.004 0.369 0.712 0.184 0.184
.V_relaci 0.029 0.020 1.444 0.149 0.793 0.793
.V_sexual 0.022 0.014 1.509 0.131 0.767 0.767
Violencia 0.056 0.034 1.637 0.102 1.000 1.0002.9.2 Estimador WLSMV
fit08_b <- cfa(model = model08,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -5.666205e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 32 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 76
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 147.253 185.018
Degrees of freedom 130 130
P-value (Chi-square) 0.143 0.001
Scaling correction factor 1.177
Shift parameter 59.913
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6023.208 2620.833
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 2.379
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997 0.978
Tucker-Lewis Index (TLI) 0.997 0.974
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.020 0.036
90 Percent confidence interval - lower 0.000 0.023
90 Percent confidence interval - upper 0.035 0.047
P-value RMSEA <= 0.05 1.000 0.980
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.094 0.094
Weighted Root Mean Square Residual:
WRMR 0.845 0.845
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.745 0.745
Cadri9a 0.848 0.071 11.964 0.000 0.632 0.632
Cadri12a 1.036 0.064 16.094 0.000 0.772 0.772
Cadri17a 1.129 0.070 16.174 0.000 0.841 0.841
Cadri24a 0.918 0.061 14.991 0.000 0.684 0.684
Cadri28a 0.940 0.068 13.757 0.000 0.700 0.700
Cadri32a 0.993 0.062 15.968 0.000 0.740 0.740
V_fisica =~
Cadri8a 1.000 0.839 0.839
Cadri25a 0.934 0.074 12.618 0.000 0.784 0.784
Cadri30a 1.052 0.085 12.332 0.000 0.882 0.882
Cadri34a 0.982 0.076 12.949 0.000 0.823 0.823
C_amenaz =~
Cadri31a 1.000 0.750 0.750
Cadri33a 1.331 0.169 7.876 0.000 0.998 0.998
V_relaci =~
Cadri20a 1.000 0.853 0.853
Cadri21a 1.018 0.140 7.268 0.000 0.868 0.868
Cadri35a 0.806 0.104 7.722 0.000 0.687 0.687
V_sexual =~
Cadri2a 1.000 0.596 0.596
Cadri19a 1.331 0.301 4.420 0.000 0.793 0.793
Violencia =~
V_fisica 1.000 0.874 0.874
V_verbal 0.887 0.101 8.822 0.000 0.872 0.872
C_amenaz 0.999 0.139 7.200 0.000 0.976 0.976
V_relaci 0.836 0.133 6.283 0.000 0.719 0.719
V_sexual 0.483 0.104 4.637 0.000 0.594 0.594
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
.V_verbal 0.000 0.000 0.000
.V_fisica 0.000 0.000 0.000
.C_amenaz 0.000 0.000 0.000
.V_relaci 0.000 0.000 0.000
.V_sexual 0.000 0.000 0.000
Violencia 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.445 0.445 0.445
.Cadri9a 0.601 0.601 0.601
.Cadri12a 0.404 0.404 0.404
.Cadri17a 0.292 0.292 0.292
.Cadri24a 0.533 0.533 0.533
.Cadri28a 0.510 0.510 0.510
.Cadri32a 0.452 0.452 0.452
.Cadri8a 0.297 0.297 0.297
.Cadri25a 0.386 0.386 0.386
.Cadri30a 0.222 0.222 0.222
.Cadri34a 0.322 0.322 0.322
.Cadri31a 0.438 0.438 0.438
.Cadri33a 0.003 0.003 0.003
.Cadri20a 0.273 0.273 0.273
.Cadri21a 0.246 0.246 0.246
.Cadri35a 0.528 0.528 0.528
.Cadri2a 0.645 0.645 0.645
.Cadri19a 0.371 0.371 0.371
.V_verbal 0.133 0.049 2.700 0.007 0.239 0.239
.V_fisica 0.166 0.054 3.065 0.002 0.237 0.237
.C_amenaz 0.027 0.047 0.570 0.568 0.048 0.048
.V_relaci 0.351 0.094 3.748 0.000 0.483 0.483
.V_sexual 0.230 0.086 2.659 0.008 0.648 0.648
Violencia 0.537 0.086 6.261 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.9.3 Fiabilidad del Modelo WLSMV en segundo orden
For constructs with categorical indicators, the alpha and the average variance extracted are calculated from polychoric (polyserial) correlations, not from Pearson correlations.
Higher-order factors were ignored. V_verbal V_fisica C_amenaz V_relaci V_sexual
alpha 0.8869414 0.9004611 0.8562722 0.8443092 0.6416203
omega 0.8207374 0.7758540 0.7471913 0.6761243 0.5205314
omega2 0.8207374 0.7758540 0.7471913 0.6761243 0.5205314
omega3 0.8262074 0.7763676 0.7471911 0.6737246 0.5205319
avevar 0.5374648 0.6934609 0.7795125 0.6507830 0.4917860 omegaL1 omegaL2 partialOmegaL1
0.8709181 0.9127616 0.9509889 2.9.4 Gráfico
semPlot::semPaths(fit08_b, whatLabels = "std", label.cex= 1.2,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 8, sizeLat2 = 3,
sizeMan = 8, sizeMan2 = 3, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 6, 1, 6), residScale = 10,
manifests = rev(fit08_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 08 Segundo orden basado en modelo 04 fork")
2.10 Modelo 09
Este modelo de segundo orden está basado en el Modelo 07 que no considera al factor de conducta amenzante.
model09 <- "# Modelo de medición
V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri31a + Cadri33a
V_relaci =~ Cadri20a + Cadri21a + Cadri35a
V_sexual =~ Cadri2a + Cadri19a
# Segundo orden
Violencia =~ V_fis_am + V_verbal + V_relaci + V_sexual"2.10.1 Estimador MLR
lavaan WARNING: some estimated lv variances are negativelavaan 0.6-6 ended normally after 99 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 40
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 354.028 208.290
Degrees of freedom 131 131
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.700
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1844.236 940.536
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 1.961
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.868 0.902
Tucker-Lewis Index (TLI) 0.846 0.885
Robust Comparative Fit Index (CFI) 0.915
Robust Tucker-Lewis Index (TLI) 0.901
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3315.327 -3315.327
Scaling correction factor 7.061
for the MLR correction
Loglikelihood unrestricted model (H1) -3138.313 -3138.313
Scaling correction factor 2.954
for the MLR correction
Akaike (AIC) 6710.654 6710.654
Bayesian (BIC) 6862.130 6862.130
Sample-size adjusted Bayesian (BIC) 6735.253 6735.253
Root Mean Square Error of Approximation:
RMSEA 0.072 0.043
90 Percent confidence interval - lower 0.063 0.034
90 Percent confidence interval - upper 0.081 0.051
P-value RMSEA <= 0.05 0.000 0.933
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.041
90 Percent confidence interval - upper 0.069
Standardized Root Mean Square Residual:
SRMR 0.061 0.061
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.542 0.702
Cadri9a 0.758 0.103 7.375 0.000 0.411 0.534
Cadri12a 0.896 0.101 8.899 0.000 0.485 0.690
Cadri17a 0.752 0.085 8.859 0.000 0.407 0.713
Cadri24a 0.627 0.098 6.425 0.000 0.340 0.591
Cadri28a 0.911 0.102 8.921 0.000 0.494 0.625
Cadri32a 0.872 0.092 9.506 0.000 0.473 0.681
V_fis_am =~
Cadri8a 1.000 0.261 0.702
Cadri25a 0.740 0.233 3.176 0.001 0.194 0.542
Cadri30a 1.213 0.271 4.472 0.000 0.317 0.744
Cadri34a 0.866 0.219 3.954 0.000 0.226 0.621
Cadri31a 0.329 0.202 1.624 0.104 0.086 0.390
Cadri33a 0.658 0.160 4.107 0.000 0.172 0.642
V_relaci =~
Cadri20a 1.000 0.196 0.578
Cadri21a 1.774 0.440 4.034 0.000 0.348 0.717
Cadri35a 1.132 0.390 2.903 0.004 0.222 0.568
V_sexual =~
Cadri2a 1.000 0.178 0.394
Cadri19a 2.838 1.670 1.699 0.089 0.505 0.651
Violencia =~
V_fis_am 1.000 0.595 0.595
V_verbal 3.621 1.569 2.307 0.021 1.039 1.039
V_relaci 0.786 0.462 1.700 0.089 0.622 0.622
V_sexual 0.464 0.310 1.498 0.134 0.406 0.406
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.302 0.037 8.071 0.000 0.302 0.507
.Cadri9a 0.423 0.049 8.585 0.000 0.423 0.715
.Cadri12a 0.260 0.041 6.328 0.000 0.260 0.524
.Cadri17a 0.160 0.024 6.696 0.000 0.160 0.491
.Cadri24a 0.215 0.024 8.860 0.000 0.215 0.650
.Cadri28a 0.380 0.044 8.584 0.000 0.380 0.609
.Cadri32a 0.258 0.038 6.805 0.000 0.258 0.536
.Cadri8a 0.070 0.014 4.924 0.000 0.070 0.508
.Cadri25a 0.090 0.020 4.439 0.000 0.090 0.706
.Cadri30a 0.081 0.031 2.664 0.008 0.081 0.447
.Cadri34a 0.081 0.022 3.624 0.000 0.081 0.614
.Cadri31a 0.041 0.024 1.701 0.089 0.041 0.848
.Cadri33a 0.042 0.016 2.717 0.007 0.042 0.588
.Cadri20a 0.077 0.029 2.626 0.009 0.077 0.666
.Cadri21a 0.115 0.033 3.436 0.001 0.115 0.486
.Cadri35a 0.104 0.031 3.373 0.001 0.104 0.678
.Cadri2a 0.172 0.044 3.892 0.000 0.172 0.845
.Cadri19a 0.346 0.171 2.026 0.043 0.346 0.576
.V_verbal -0.023 0.089 -0.262 0.793 -0.079 -0.079
.V_fis_am 0.044 0.021 2.096 0.036 0.646 0.646
.V_relaci 0.024 0.016 1.521 0.128 0.613 0.613
.V_sexual 0.026 0.018 1.488 0.137 0.835 0.835
Violencia 0.024 0.017 1.408 0.159 1.000 1.0002.10.2 Estimador WLSMV
fit09_b <- cfa(model = model09,
data = cadri_data,
ordered = TRUE,
estimator = "WLSMV",
mimic = "Mplus")lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -2.408315e-17) is smaller than zero. This may be a symptom that
the model is not identified.lavaan 0.6-6 ended normally after 35 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 75
Number of observations 326
Model Test User Model:
Standard Robust
Test Statistic 133.336 175.084
Degrees of freedom 131 131
P-value (Chi-square) 0.427 0.006
Scaling correction factor 1.164
Shift parameter 60.551
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 6023.208 2620.833
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 2.379
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 0.982
Tucker-Lewis Index (TLI) 1.000 0.979
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.007 0.032
90 Percent confidence interval - lower 0.000 0.018
90 Percent confidence interval - upper 0.028 0.044
P-value RMSEA <= 0.05 1.000 0.995
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.087 0.087
Weighted Root Mean Square Residual:
WRMR 0.805 0.805
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
V_verbal =~
Cadri7a 1.000 0.744 0.744
Cadri9a 0.848 0.071 11.944 0.000 0.631 0.631
Cadri12a 1.041 0.065 16.058 0.000 0.775 0.775
Cadri17a 1.130 0.069 16.363 0.000 0.841 0.841
Cadri24a 0.916 0.061 14.954 0.000 0.682 0.682
Cadri28a 0.937 0.068 13.840 0.000 0.697 0.697
Cadri32a 0.996 0.062 16.023 0.000 0.741 0.741
V_fis_am =~
Cadri8a 1.000 0.829 0.829
Cadri25a 0.934 0.073 12.816 0.000 0.774 0.774
Cadri30a 1.047 0.082 12.745 0.000 0.868 0.868
Cadri34a 0.980 0.075 13.027 0.000 0.813 0.813
Cadri31a 0.946 0.113 8.370 0.000 0.784 0.784
Cadri33a 1.133 0.086 13.221 0.000 0.940 0.940
V_relaci =~
Cadri20a 1.000 0.848 0.848
Cadri21a 1.009 0.136 7.432 0.000 0.856 0.856
Cadri35a 0.833 0.106 7.870 0.000 0.707 0.707
V_sexual =~
Cadri2a 1.000 0.595 0.595
Cadri19a 1.336 0.313 4.263 0.000 0.794 0.794
Violencia =~
V_fis_am 1.000 0.809 0.809
V_verbal 1.051 0.148 7.104 0.000 0.947 0.947
V_relaci 0.914 0.155 5.900 0.000 0.723 0.723
V_sexual 0.524 0.123 4.273 0.000 0.592 0.592
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.000 0.000 0.000
.Cadri9a 0.000 0.000 0.000
.Cadri12a 0.000 0.000 0.000
.Cadri17a 0.000 0.000 0.000
.Cadri24a 0.000 0.000 0.000
.Cadri28a 0.000 0.000 0.000
.Cadri32a 0.000 0.000 0.000
.Cadri8a 0.000 0.000 0.000
.Cadri25a 0.000 0.000 0.000
.Cadri30a 0.000 0.000 0.000
.Cadri34a 0.000 0.000 0.000
.Cadri31a 0.000 0.000 0.000
.Cadri33a 0.000 0.000 0.000
.Cadri20a 0.000 0.000 0.000
.Cadri21a 0.000 0.000 0.000
.Cadri35a 0.000 0.000 0.000
.Cadri2a 0.000 0.000 0.000
.Cadri19a 0.000 0.000 0.000
.V_verbal 0.000 0.000 0.000
.V_fis_am 0.000 0.000 0.000
.V_relaci 0.000 0.000 0.000
.V_sexual 0.000 0.000 0.000
Violencia 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a|t1 0.178 0.070 2.539 0.011 0.178 0.178
Cadri7a|t2 1.347 0.098 13.713 0.000 1.347 1.347
Cadri7a|t3 1.717 0.123 13.919 0.000 1.717 1.717
Cadri9a|t1 0.015 0.070 0.221 0.825 0.015 0.015
Cadri9a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri9a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri12a|t1 0.549 0.074 7.465 0.000 0.549 0.549
Cadri12a|t2 1.495 0.107 14.000 0.000 1.495 1.495
Cadri12a|t3 1.871 0.138 13.536 0.000 1.871 1.871
Cadri17a|t1 0.844 0.079 10.623 0.000 0.844 0.844
Cadri17a|t2 1.654 0.118 14.003 0.000 1.654 1.654
Cadri17a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri24a|t1 0.445 0.072 6.162 0.000 0.445 0.445
Cadri24a|t2 1.871 0.138 13.536 0.000 1.871 1.871
Cadri24a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri28a|t1 0.462 0.072 6.380 0.000 0.462 0.462
Cadri28a|t2 1.177 0.090 13.029 0.000 1.177 1.177
Cadri28a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri32a|t1 0.622 0.075 8.324 0.000 0.622 0.622
Cadri32a|t2 1.569 0.112 14.040 0.000 1.569 1.569
Cadri32a|t3 1.828 0.134 13.667 0.000 1.828 1.828
Cadri8a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri8a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri8a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri25a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri25a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri30a|t1 1.257 0.094 13.400 0.000 1.257 1.257
Cadri30a|t2 2.024 0.157 12.925 0.000 2.024 2.024
Cadri30a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri34a|t1 1.328 0.097 13.656 0.000 1.328 1.328
Cadri34a|t2 2.249 0.192 11.707 0.000 2.249 2.249
Cadri34a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri31a|t1 2.161 0.177 12.220 0.000 2.161 2.161
Cadri31a|t2 2.504 0.250 10.011 0.000 2.504 2.504
Cadri31a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri33a|t1 1.871 0.138 13.536 0.000 1.871 1.871
Cadri33a|t2 2.357 0.214 11.014 0.000 2.357 2.357
Cadri33a|t3 2.740 0.329 8.326 0.000 2.740 2.740
Cadri20a|t1 1.717 0.123 13.919 0.000 1.717 1.717
Cadri20a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri20a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri21a|t1 1.089 0.087 12.530 0.000 1.089 1.089
Cadri21a|t2 1.917 0.143 13.374 0.000 1.917 1.917
Cadri21a|t3 2.357 0.214 11.014 0.000 2.357 2.357
Cadri35a|t1 1.347 0.098 13.713 0.000 1.347 1.347
Cadri35a|t2 2.161 0.177 12.220 0.000 2.161 2.161
Cadri35a|t3 2.504 0.250 10.011 0.000 2.504 2.504
Cadri2a|t1 1.386 0.100 13.817 0.000 1.386 1.386
Cadri2a|t2 1.968 0.149 13.174 0.000 1.968 1.968
Cadri2a|t3 2.249 0.192 11.707 0.000 2.249 2.249
Cadri19a|t1 0.558 0.074 7.573 0.000 0.558 0.558
Cadri19a|t2 1.257 0.094 13.400 0.000 1.257 1.257
Cadri19a|t3 1.789 0.130 13.772 0.000 1.789 1.789
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Cadri7a 0.446 0.446 0.446
.Cadri9a 0.601 0.601 0.601
.Cadri12a 0.400 0.400 0.400
.Cadri17a 0.292 0.292 0.292
.Cadri24a 0.535 0.535 0.535
.Cadri28a 0.514 0.514 0.514
.Cadri32a 0.451 0.451 0.451
.Cadri8a 0.312 0.312 0.312
.Cadri25a 0.400 0.400 0.400
.Cadri30a 0.246 0.246 0.246
.Cadri34a 0.340 0.340 0.340
.Cadri31a 0.385 0.385 0.385
.Cadri33a 0.117 0.117 0.117
.Cadri20a 0.281 0.281 0.281
.Cadri21a 0.268 0.268 0.268
.Cadri35a 0.501 0.501 0.501
.Cadri2a 0.647 0.647 0.647
.Cadri19a 0.369 0.369 0.369
.V_verbal 0.057 0.065 0.882 0.378 0.103 0.103
.V_fis_am 0.238 0.063 3.751 0.000 0.346 0.346
.V_relaci 0.343 0.093 3.701 0.000 0.477 0.477
.V_sexual 0.230 0.087 2.642 0.008 0.650 0.650
Violencia 0.450 0.084 5.386 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Cadri7a 1.000 1.000 1.000
Cadri9a 1.000 1.000 1.000
Cadri12a 1.000 1.000 1.000
Cadri17a 1.000 1.000 1.000
Cadri24a 1.000 1.000 1.000
Cadri28a 1.000 1.000 1.000
Cadri32a 1.000 1.000 1.000
Cadri8a 1.000 1.000 1.000
Cadri25a 1.000 1.000 1.000
Cadri30a 1.000 1.000 1.000
Cadri34a 1.000 1.000 1.000
Cadri31a 1.000 1.000 1.000
Cadri33a 1.000 1.000 1.000
Cadri20a 1.000 1.000 1.000
Cadri21a 1.000 1.000 1.000
Cadri35a 1.000 1.000 1.000
Cadri2a 1.000 1.000 1.000
Cadri19a 1.000 1.000 1.0002.10.3 Fiabilidad del Modelo WLSMV de segundo orden
For constructs with categorical indicators, the alpha and the average variance extracted are calculated from polychoric (polyserial) correlations, not from Pearson correlations.
Higher-order factors were ignored. V_verbal V_fis_am V_relaci V_sexual
alpha 0.8869414 0.9257101 0.8443092 0.6416203
omega 0.8206861 0.8163989 0.6724028 0.5218421
omega2 0.8206861 0.8163989 0.6724028 0.5218421
omega3 0.8259776 0.8204771 0.6709403 0.5218420
avevar 0.5374292 0.7000276 0.6501830 0.4923251 omegaL1 omegaL2 partialOmegaL1
0.8537566 0.8632906 0.9486812 2.10.4 Gráfico
# semPlot::semPaths(fit09_b, whatLabels = "std", label.cex= 1.8,
# edge.label.cex = 1.3, nCharEdges = 3,
# nCharNodes = 8, sizeLat = 6, sizeLat2 = 4,
# sizeMan = 6, sizeMan2 = 2.5, rotation = 2, intercepts = F,
# thresholds = F, groups = "latents", pastel = TRUE,
# exoVar = FALSE, edge.color = "black",
# curvature = 2, mar = c(1, 6, 1, 6), residScale = 10,
# manifests = rev(fit09_b@Data@ordered))
#
semPlot::semPaths(fit09_b, whatLabels = "std", label.cex= 1.2,
edge.label.cex = 0.8, nCharEdges = 3,
nCharNodes = 8, sizeLat = 8, sizeLat2 = 3,
sizeMan = 8, sizeMan2 = 3, rotation = 2, intercepts = F,
thresholds = F, groups = "latents", pastel = TRUE,
exoVar = FALSE, edge.color = "black",
curvature = 2, mar = c(1, 6, 1, 6), residScale = 10,
manifests = rev(fit09_b@Data@ordered))
title("Modelo Factorial del CADRI \n Modelo 09 Segundo orden basado en modelo 07")
2.11 Comparación de los modelos factoriales
Mediante el uso de una función personalizada, se realiza la comparación de los índices de ajustes de todos los modelos, tanto por MLR como por WLSMV.
2.11.1 Comparación de modelos MLR
compare_fit_mlr <- bind_rows(
glance.lavaan.fix(fit01_a),
glance.lavaan.fix(fit02_a),
glance.lavaan.fix(fit03_a),
glance.lavaan.fix(fit04_a),
glance.lavaan.fix(fit04_a_fork),
glance.lavaan.fix(fit05_a),
glance.lavaan.fix(fit06_a),
glance.lavaan.fix(fit07_a),
glance.lavaan.fix(fit08_a),
glance.lavaan.fix(fit09_a),
.id = "Modelo"
) %>%
mutate(Modelo = fct_recode(Modelo,
"Modelo 01" = "1",
"Modelo 02" = "2",
"Modelo 03" = "3",
"Modelo 04" = "4",
"Modelo 04 fork" = "5",
"Modelo 05" = "6",
"Modelo 06" = "7",
"Modelo 07" = "8",
"Modelo 08" = "9",
"Modelo 09" = "10")) %>%
mutate_if(is.numeric, round, 3)2.11.2 Comparación de modelos WLSMV
compare_fit_wlsmv <- bind_rows(
glance.lavaan.fix(fit01_b),
glance.lavaan.fix(fit02_b),
glance.lavaan.fix(fit03_b),
glance.lavaan.fix(fit04_b),
glance.lavaan.fix(fit04_b_fork),
glance.lavaan.fix(fit05_b),
glance.lavaan.fix(fit06_b),
glance.lavaan.fix(fit07_b),
glance.lavaan.fix(fit08_b),
glance.lavaan.fix(fit09_b),
.id = "Modelo"
) %>%
mutate(Modelo = fct_recode(Modelo,
"Modelo 01" = "1",
"Modelo 02" = "2",
"Modelo 03" = "3",
"Modelo 04" = "4",
"Modelo 04 fork" = "5",
"Modelo 05" = "6",
"Modelo 06" = "7",
"Modelo 07" = "8",
"Modelo 08" = "9",
"Modelo 09" = "10")) %>%
mutate_if(is.numeric, round, 3)---
title: "Análisis AFC del CADRI"
author: "Brian N. Peña Calero"
date: "20/5/2020"
output: 
  html_notebook: 
    highlight: zenburn
    number_sections: yes
    theme: sandstone 
    toc: yes
---

```{r include=FALSE}
options(tidyverse.quiet = TRUE)
```


Se cargan las librerías a utilizar para el presente análisis

```{r}
library(tidyverse)
library(lavaan)
```

# Importación de datos y tratamiento de datos

```{r}
data1 <- haven::read_sav("1. BD/BD_1ER ENVIO.sav")
data2 <- haven::read_sav("1. BD/BD_Colegio Jose Granda.sav")
```


## Guardar las etiquetas
Se guardan las etiquetas asociadas a los datos provenientes de la codificación en el SPSS
```{r}
library(labelled)

labels_cadri <- data1 %>% 
  var_label(unlist = TRUE) %>% 
  enframe() %>% 
  mutate(
    value = str_remove(value, "DURANTE LAS DISCUSIONES EN LOS ÚLTIMOS 12 MESES ")
  )
```

```{r echo = FALSE}
labels_cadri %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(fixedColumns = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```


## Unificar data

Se juntan las 2 base de datos. Además se elimina data missing y se transforma los ítems a variables númericas (para fines de análisis).

```{r}
data1 <- data1 %>% 
  select(Cedula, Sexo:Cadri35b) %>% 
  mutate(Colegio = "Colegio Independencia") 

data2 <- data2 %>% 
  select(Cedula, Colegio:Cadri35b)

cadri_data <- rbind(data1, data2)

cadri_data <- cadri_data %>% 
  drop_na(Cadri1a:Cadri35b)

cadri_data <- cadri_data %>% 
  mutate(across(Cadri1a:Cadri35b, as.numeric))
```

```{r echo = FALSE}
cadri_data %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(scrollY = 550,
                   scrollX = 500,
                   fixedColumns = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

## Descriptivos

Estadísticos descriptivos para la parte "a" de los ítems (perpetrador)
```{r}
descriptivos_a <- cadri_data %>% 
  select(ends_with("a") & starts_with("Cadri")) %>% 
  psych::describe() %>% 
  mutate_if(is.numeric, round, 3)
```

```{r echo=FALSE}
descriptivos_a %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(scrollY = 360,
                   scrollX = 500,
                   autoWidth = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

Estadísticos descriptivos para la parte "b" de los ítems (violencia sufrida)
```{r}
descriptivos_b <- cadri_data %>% 
  select(ends_with("b") & starts_with("Cadri")) %>% 
  psych::describe() %>% 
  mutate_if(is.numeric, round, 3)
```

```{r echo=FALSE}
descriptivos_b %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(scrollY = 360,
                   scrollX = 500,
                   autoWidth = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

### Gráficos

Visualización de los descriptivos a y b

```{r fig.height = 7.5, fig.width = 12, dpi = 300, fig.align = "center"}
cadri_data %>% 
  select(ends_with("a") & starts_with("Cadri")) %>% 
  pivot_longer(
    cols = everything(),
    names_to = "Items",
    values_to = "Puntaje"
  ) %>% 
  mutate(
    Items = as_factor(Items),
    Puntaje = factor(Puntaje, 
                     levels = c(4, 3, 2, 1),
                     labels = c("6 o más veces",
                                "3 a 5 veces",
                                "1 a 2 veces",
                                "Nunca"))
    ) %>% 
  count(Items, Puntaje) %>% 
  ggplot(aes(x = Puntaje, y = n)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~ Items) +
  labs(title = "Descriptivo de los ítems del CADRI - Violencia Cometida",
       y = "",
       x = "Participantes") +
  theme_bw()
```



```{r fig.height = 7.5, fig.width = 12, dpi = 300, fig.align = "center"}
cadri_data %>% 
  select(ends_with("b") & starts_with("Cadri")) %>% 
  pivot_longer(
    cols = everything(),
    names_to = "Items",
    values_to = "Puntaje"
  ) %>% 
  mutate(
    Items = as_factor(Items),
    Puntaje = factor(Puntaje, 
                     levels = c(4, 3, 2, 1),
                     labels = c("6 o más veces",
                                "3 a 5 veces",
                                "1 a 2 veces",
                                "Nunca"))
    ) %>% 
  count(Items, Puntaje) %>% 
  ggplot(aes(x = Puntaje, y = n)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~ Items) +
  labs(title = "Descriptivo de los ítems del CADRI - Violencia Recibida",
       y = "",
       x = "Participantes") +
  theme_bw()
```


# Análisis Factoriales Confirmatorios

La escala CADRI tiene 5 factores y se corresponden con los items de la siguiente manera:

- Violencia verbal: 4,7,9,12,17,21,23,24,28 y 32 = 10 items
- Violencia física: 8, 25, 30 y 34 = 4 items
- Conducta amenazante: 5, 29, 31 y 33 =4 items
- Violencia relacional: 3, 20, 35 = 3 items
- Violencia sexual: 2, 13, 15 y 19 = 4 items
- Items sobre solución de conflcitos: 1, 6, 10, 11, 14, 16, 18, 22, 26 y 27 =10 items

***Nota***: Todos los modelos tienen 2 análisis `fit_a` y `fit_b` que se diferencian únicamente por el estimador utilizado para su evaluación. El `fit_a` utiliza el estimador `MLR` que es ideal para data contínua y problemas de normalidad, mientras que el `fit_b` utiliza el estimador `WLSMV`que es el mejor estimador en consideración de la ordinalidad de los datos.

## Modelo 01
 El primer modelo probará todos los ítems tal cual se encuentran descritos en el instrumento original.

```{r}
model01 <- "# Modelo de medición
            V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
                        Cadri24a + Cadri28a + Cadri32a
            V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
            C_amenaz =~ Cadri5a + Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri3a + Cadri20a + Cadri35a
            V_sexual =~ Cadri2a + Cadri13a + Cadri15a + Cadri19a
            R_confli =~ Cadri1a + Cadri6a + Cadri10a + Cadri11a + Cadri14a + Cadri16a + Cadri18a +
                        Cadri22a + Cadri26a + Cadri27a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}

fit01_a <- cfa(model = model01,
               data = cadri_data,
               estimator = "MLR")

summary(fit01_a, fit.measures = TRUE, standardized = TRUE)
```


### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}

fit01_b <- cfa(model = model01,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit01_b, fit.measures = TRUE, standardized = TRUE)
```

### Índices de Modificación (`fit_b`)

```{r}
modindices(fit01_b, sort. = TRUE, maximum.number = 20)
```

### Gráfico

```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
## Graficar
semPlot::semPaths(fit01_b, whatLabels = "std", label.cex= 1.2,
                  edge.label.cex = 0.5, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 6, sizeLat2 = 4,
                  sizeMan = 6, sizeMan2 = 1.5, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 12, 2, 12), residScale = 10, 
                  manifests = rev(fit01_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 01")
```


## Modelo 02

A partir del análisis descriptivo y la observación del modelo 01, se hacen los siguientes cambios:

- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)

```{r}
model02 <- "# Modelo de medición
            V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
                        Cadri24a + Cadri28a + Cadri32a
            V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
            C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri3a + Cadri20a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a
            R_confli =~ Cadri1a + Cadri6a + Cadri10a + Cadri11a + Cadri14a + Cadri16a + Cadri18a +
                        Cadri22a + Cadri26a + Cadri27a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit02_a <- cfa(model = model02,
               data = cadri_data,
               estimator = "MLR")

summary(fit02_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit02_a, sort. = TRUE, maximum.number = 20)
```


### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit02_b <- cfa(model = model02,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit02_b, fit.measures = TRUE, standardized = TRUE)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
## Graficar
semPlot::semPaths(fit02_b, whatLabels = "std", label.cex= 1.2,
                  edge.label.cex = 0.5, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 6, sizeLat2 = 4,
                  sizeMan = 6, sizeMan2 = 1.5, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 12, 2, 12), residScale = 10, 
                  manifests = rev(fit02_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 02")
```


## Modelo 03

Se mantienen los cambios del Modelo 02:
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)

Se adiciona el siguiente cambio:
- No considerar al factor Resolución de conflictos como parte del modelo factorial

```{r}
model03 <- "# Modelo de medición
            V_verbal =~ Cadri4a + Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + Cadri23a +
                        Cadri24a + Cadri28a + Cadri32a
            V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
            C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri3a + Cadri20a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit03_a <- cfa(model = model03,
               data = cadri_data,
               estimator = "MLR")

summary(fit03_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit03_a, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit03_b <- cfa(model = model03,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit03_b, fit.measures = TRUE, standardized = TRUE)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
## Graficar
semPlot::semPaths(fit03_b, whatLabels = "std", label.cex= 1.2,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.5, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 2, 15), residScale = 10, 
                  manifests = rev(fit03_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 03")
```

### Notas de análisis
**Análisis del modelo 02 y 03**

- Cargas factoriales menores a 0.45:
  - Violencia verbal
    - Item 04
    - Item 23
  - Conducta amenzante
    - Item 05
    - Item 29
    - Item 33

- Violencia relacional
  - Item 03 (aparece un sinsentido en los índices de modificación)

- Conducta amenzante (Correlación mayor a 1): Violencia verbal, física y amenzante

- Análisis de Índices de modificación:
  - Resolución de conflictos:	Correlación: Item 16 y 26
  - Correlación interfactores: Item 8 (violencia física) y 33 (Conducta amenazante de violencia física)
  - Item 23 no guarda relación con constructo. además está observada por baja carga factorial.


## Modelo 04

Se mantienen los cambios del Modelo 02 y 03:
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)
- No considerar al factor Resolución de conflictos como parte del modelo factorial

Se adiciona el siguiente cambio:
- Sin item 04 y 23 (V. Verbal)
- Sin item 03 (V. relacional)

```{r}
model04 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + 
                        Cadri24a + Cadri28a + Cadri32a
            V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
            C_amenaz =~ Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit04_a <- cfa(model = model04,
               data = cadri_data,
               estimator = "MLR")

summary(fit04_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit04_a, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit04_b <- cfa(model = model04,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit04_b, fit.measures = TRUE, standardized = TRUE)

modindices(fit04_b, sort. = TRUE, maximum.number = 20)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit04_b, whatLabels = "std", label.cex= 1.3,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 2, 15), residScale = 10, 
                  manifests = rev(fit04_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 04")
```



## Modelo 04 Fork

Se mantienen los cambios de los modelos anteriores
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)
- No considerar al factor Resolución de conflictos como parte del modelo factorial
- Sin item 04 y 23 (V. Verbal)
- Sin item 03 (V. relacional)

Se incorpora los cambios que se encontrarán en el modelo 07 posteriormente:
- Teórica y empíricamente el ítem 21 afecta directamente a las relaciones de la persona
- Quitar el ítem 29. Presenta carga compuesta

```{r}
model04_fork <- "# Modelo de medición
                 V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a +  
                             Cadri24a + Cadri28a + Cadri32a
                 V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
                 C_amenaz =~ Cadri31a + Cadri33a
                 V_relaci =~ Cadri20a + Cadri21a + Cadri35a
                 V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit04_a_fork <- cfa(model = model04_fork,
                    data = cadri_data,
                    estimator = "MLR")

summary(fit04_a_fork, fit.measures = TRUE, standardized = TRUE)

modindices(fit04_a_fork, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit04_b_fork <- cfa(model = model04_fork,
                    data = cadri_data,
                    ordered = TRUE,
                    estimator = "WLSMV",
                    mimic = "Mplus")

summary(fit04_b_fork, fit.measures = TRUE, standardized = TRUE)

modindices(fit04_b_fork, sort. = TRUE, maximum.number = 20)
```

### Fiabilidad del modelo categórico
```{r}
semTools::reliability(fit04_b_fork)
```


### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit04_b_fork, whatLabels = "std", label.cex= 1.3,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 2, 15), residScale = 10, 
                  manifests = rev(fit04_b_fork@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 04 con ajustes del modelo 07")

```


## Modelo 05

Se mantienen los cambios de los modelos 01 al 04
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)
- No considerar al factor Resolución de conflictos como parte del modelo factorial
- Sin item 04 y 23 (V. Verbal)
- Sin item 03 (V. relacional)

Se adiciona el siguiente cambio:
- Unir factor de conducta amenzante con violencia física debido a que el contenido de los ítems hacen alusión a la intención de hacer un daño físico. Esto es mejor a generar errores correlacionados entre ítems que pertenecen a diferentes dimensiones.

```{r}
model05 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri21a + 
                        Cadri24a + Cadri28a + Cadri32a
            V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit05_a <- cfa(model = model05,
               data = cadri_data,
               estimator = "MLR")

summary(fit05_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit05_a, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit05_b <- cfa(model = model05,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit05_b, fit.measures = TRUE, standardized = TRUE)

modindices(fit05_b, sort. = TRUE, maximum.number = 20)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit05_b, whatLabels = "std", label.cex= 1.3,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 2, 15), residScale = 10, 
                  manifests = rev(fit05_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 05. Conducta amenzante y física juntos")
```



## Modelo 06

Se mantienen los cambios de los modelos 01 al 04 y 05
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)
- No considerar al factor Resolución de conflictos como parte del modelo factorial
- Sin item 04 y 23 (V. Verbal)
- Sin item 03 (V. relacional)
- Unir factor de conducta amenzante con violencia física 

Se adiciona el siguiente cambio:
- Ítem 21 pasa a violencia relacional: Su contenido hace referencia directamente a las relaciones de las personas

*Nota*: Alerta con el ítem 29. Puede tener sesgo por deseabilidad social (el modelo 07 será sin este ítem)

```{r}
model06 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
            V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri29a + Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri21a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit06_a <- cfa(model = model06,
               data = cadri_data,
               estimator = "MLR")

summary(fit06_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit06_a, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit06_b <- cfa(model = model06,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit06_b, fit.measures = TRUE, standardized = TRUE)

modindices(fit06_b, sort. = TRUE, maximum.number = 20)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit06_b, whatLabels = "std", label.cex= 1.3,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 7, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 1, 15), residScale = 10, 
                  manifests = rev(fit06_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 06 basado en modelo 05")
```



## Modelo 07

Se mantienen los cambios de los modelos 01 al 04 y 05
- Sin item 15, 13  (V Sexual)
- Sin item 05 (Conducta amenzante)
- No considerar al factor Resolución de conflictos como parte del modelo factorial
- Sin item 04 y 23 (V. Verbal)
- Sin item 03 (V. relacional)
- Unir factor de conducta amenzante con violencia física 
- Ítem 21 pasa a violencia relacional

Se adiciona el siguiente cambio:
- Quitar el ítem 29. Presenta carga compuesta

```{r}
model07 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
            V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri21a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit07_a <- cfa(model = model07,
               data = cadri_data,
               estimator = "MLR")

summary(fit07_a, fit.measures = TRUE, standardized = TRUE)

modindices(fit07_a, sort. = TRUE, maximum.number = 20)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit07_b <- cfa(model = model07,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit07_b, fit.measures = TRUE, standardized = TRUE)

modindices(fit07_b, sort. = TRUE, maximum.number = 20)
```

### Fiabilidad del modelo WLSMV

```{r}
semTools::reliability(fit07_b)
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit07_b, whatLabels = "std", label.cex= 1.3,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 8, sizeLat2 = 4,
                  sizeMan = 7, sizeMan2 = 2.8, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 15, 1, 15), residScale = 10, 
                  manifests = rev(fit07_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 07 basado en modelo 05")
```


## Modelo 08: Segundo orden

Este modelo de segundo orden está basado en el `Modelo 04 Fork` que incluye los cambios encontrados en el modelo 07 con respecto a los ítems, pero considerando al factor de conducta amenzante.

```{r}
model08 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a +  
                        Cadri24a + Cadri28a + Cadri32a
            V_fisica =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a
            C_amenaz =~ Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri21a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a
            # Segundo orden
            Violencia =~ V_fisica + V_verbal + C_amenaz + V_relaci + V_sexual"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit08_a <- cfa(model = model08,
               data = cadri_data,
               estimator = "MLR")

summary(fit08_a, fit.measures = TRUE, standardized = TRUE)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit08_b <- cfa(model = model08,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit08_b, fit.measures = TRUE, standardized = TRUE)

modindices(fit08_b, sort. = TRUE, maximum.number = 20)
```

### Fiabilidad del Modelo WLSMV en segundo orden

```{r}
semTools::reliability(fit08_b)
semTools::reliabilityL2(fit08_b, "Violencia")
```

### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit08_b, whatLabels = "std", label.cex= 1.2,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 8, sizeLat2 = 3,
                  sizeMan = 8, sizeMan2 = 3, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 6, 1, 6), residScale = 10, 
                  manifests = rev(fit08_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 08 Segundo orden basado en modelo 04 fork")
```

## Modelo 09

Este modelo de segundo orden está basado en el `Modelo 07` que no considera al factor de conducta amenzante.

```{r}
model09 <- "# Modelo de medición
            V_verbal =~ Cadri7a + Cadri9a + Cadri12a + Cadri17a + Cadri24a + Cadri28a + Cadri32a
            V_fis_am =~ Cadri8a + Cadri25a + Cadri30a + Cadri34a + Cadri31a + Cadri33a
            V_relaci =~ Cadri20a + Cadri21a + Cadri35a
            V_sexual =~ Cadri2a + Cadri19a
            # Segundo orden
            Violencia =~ V_fis_am + V_verbal + V_relaci + V_sexual"
```

### Estimador MLR
```{r, attr.output='style="max-height: 500px;"'}
fit09_a <- cfa(model = model09,
               data = cadri_data,
               estimator = "MLR")

summary(fit09_a, fit.measures = TRUE, standardized = TRUE)
```

### Estimador WLSMV
```{r, attr.output='style="max-height: 500px;"'}
fit09_b <- cfa(model = model09,
               data = cadri_data,
               ordered = TRUE,
               estimator = "WLSMV",
               mimic = "Mplus")

summary(fit09_b, fit.measures = TRUE, standardized = TRUE)
```

### Fiabilidad del Modelo WLSMV de segundo orden

```{r}
semTools::reliability(fit09_b)
semTools::reliabilityL2(fit09_b, "Violencia")
```


### Gráfico
```{r fig.height = 12, fig.width = 15, dpi = 300, fig.align = "center"}
semPlot::semPaths(fit09_b, whatLabels = "std", label.cex= 1.2,
                  edge.label.cex = 0.8, nCharEdges = 3,
                  nCharNodes = 8, sizeLat = 8, sizeLat2 = 3,
                  sizeMan = 8, sizeMan2 = 3, rotation = 2, intercepts = F,
                  thresholds = F, groups = "latents", pastel = TRUE,
                  exoVar = FALSE, edge.color = "black",
                  curvature = 2, mar = c(1, 6, 1, 6), residScale = 10, 
                  manifests = rev(fit09_b@Data@ordered))

title("Modelo Factorial del CADRI \n Modelo 09 Segundo orden basado en modelo 07")
```


## Comparación de los modelos factoriales

Mediante el uso de una función personalizada, se realiza la comparación de los índices de ajustes de todos los modelos, tanto por `MLR` como por `WLSMV`.

```{r}
source("Function glance lavaan fix.R")
```

### Comparación de modelos `MLR`

```{r}
compare_fit_mlr <- bind_rows(
  glance.lavaan.fix(fit01_a),
  glance.lavaan.fix(fit02_a),
  glance.lavaan.fix(fit03_a),
  glance.lavaan.fix(fit04_a),
  glance.lavaan.fix(fit04_a_fork),
  glance.lavaan.fix(fit05_a),
  glance.lavaan.fix(fit06_a),
  glance.lavaan.fix(fit07_a),
  glance.lavaan.fix(fit08_a),
  glance.lavaan.fix(fit09_a),
  .id = "Modelo"
) %>% 
  mutate(Modelo = fct_recode(Modelo,
                             "Modelo 01" = "1",
                             "Modelo 02" = "2",
                             "Modelo 03" = "3",
                             "Modelo 04" = "4",
                             "Modelo 04 fork" = "5",
                             "Modelo 05" = "6",
                             "Modelo 06" = "7",
                             "Modelo 07" = "8",
                             "Modelo 08" = "9",
                             "Modelo 09" = "10")) %>% 
  mutate_if(is.numeric, round, 3)
```

```{r echo=FALSE}
compare_fit_mlr %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(scrollY = 380,
                   scrollX = 500,
                   autoWidth = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

### Comparación de modelos `WLSMV`

```{r}
compare_fit_wlsmv <- bind_rows(
  glance.lavaan.fix(fit01_b),
  glance.lavaan.fix(fit02_b),
  glance.lavaan.fix(fit03_b),
  glance.lavaan.fix(fit04_b),
  glance.lavaan.fix(fit04_b_fork),
  glance.lavaan.fix(fit05_b),
  glance.lavaan.fix(fit06_b),
  glance.lavaan.fix(fit07_b),
  glance.lavaan.fix(fit08_b),
  glance.lavaan.fix(fit09_b),
  .id = "Modelo"
) %>% 
  mutate(Modelo = fct_recode(Modelo,
                             "Modelo 01" = "1",
                             "Modelo 02" = "2",
                             "Modelo 03" = "3",
                             "Modelo 04" = "4",
                             "Modelo 04 fork" = "5",
                             "Modelo 05" = "6",
                             "Modelo 06" = "7",
                             "Modelo 07" = "8",
                             "Modelo 08" = "9",
                             "Modelo 09" = "10")) %>% 
  mutate_if(is.numeric, round, 3)
```

```{r echo=FALSE}
compare_fit_wlsmv %>%  
  DT::datatable(
    rownames = FALSE,
    filter = "top",
    style = "bootstrap4",
    class = 'cell-border stripe',
    extensions = 'Buttons', 
    options = list(scrollY = 380,
                   scrollX = 500,
                   autoWidth = TRUE,
                   dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

